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deleted file mode 100644
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--- a/3d_segmentation/unet_segmentation_3d_catalyst.ipynb
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@@ -1,659 +0,0 @@
-{
- "cells": [
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "4bkNVc42_H9r"
-   },
-   "source": [
-    "Copyright (c) MONAI Consortium  \n",
-    "Licensed under the Apache License, Version 2.0 (the \"License\");  \n",
-    "you may not use this file except in compliance with the License.  \n",
-    "You may obtain a copy of the License at  \n",
-    "    http://www.apache.org/licenses/LICENSE-2.0  \n",
-    "Unless required by applicable law or agreed to in writing, software  \n",
-    "distributed under the License is distributed on an \"AS IS\" BASIS,  \n",
-    "WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.  \n",
-    "See the License for the specific language governing permissions and  \n",
-    "limitations under the License.\n",
-    "\n",
-    "# 3D segmentation with [MONAI](https://github.com/Project-MONAI/MONAI) and [Catalyst](https://github.com/catalyst-team/catalyst)\n",
-    "\n",
-    "This tutorial demonstrates how [MONAI](https://github.com/Project-MONAI/MONAI) can be used with the [Catalyst](https://github.com/catalyst-team/catalyst) framework for 3D segmentation task.\n",
-    "And easily use below features:\n",
-    "\n",
-    "* Prepare synthetic data.\n",
-    "* Load Nifti image with metadata.\n",
-    "* Transforms for dictionary format data.\n",
-    "* Add channel dim to the data if no channel dimension.\n",
-    "* Scale medical image intensity with expected range.\n",
-    "* Crop out a batch of balanced images based on positive / negative label ratio.\n",
-    "* 3D UNet model, Dice loss function, Mean Dice metric for 3D segmentation task.\n",
-    "* Sliding window inference method.\n",
-    "* Deterministic training for reproducibility.\n",
-    "\n",
-    "This tutorial is based on [unet_training_dict.py](torch/unet_training_dict.py) and [spleen_segmentation_3d.ipynb](spleen_segmentation_3d.ipynb).\n",
-    "\n",
-    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/Project-MONAI/tutorials/blob/main/3d_segmentation/unet_segmentation_3d_catalyst.ipynb)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "hGYGdy1yBLr5"
-   },
-   "source": [
-    "## Setup environment"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "!python -c \"import monai\" || pip install -q \"monai-weekly[nibabel, tensorboard]\"\n",
-    "!python -c \"import matplotlib\" || pip install -q matplotlib\n",
-    "!python -c \"import catalyst\" || pip install -q catalyst==20.07\n",
-    "%matplotlib inline"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Setup imports"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "MONAI version: 0.9.1\n",
-      "Numpy version: 1.22.4\n",
-      "Pytorch version: 1.13.0a0+340c412\n",
-      "MONAI flags: HAS_EXT = True, USE_COMPILED = False, USE_META_DICT = False\n",
-      "MONAI rev id: 356d2d2f41b473f588899d705bbc682308cee52c\n",
-      "MONAI __file__: /opt/monai/monai/__init__.py\n",
-      "\n",
-      "Optional dependencies:\n",
-      "Pytorch Ignite version: 0.4.9\n",
-      "Nibabel version: 4.0.1\n",
-      "scikit-image version: 0.19.3\n",
-      "Pillow version: 9.0.1\n",
-      "Tensorboard version: 2.9.1\n",
-      "gdown version: 4.5.1\n",
-      "TorchVision version: 0.13.0a0\n",
-      "tqdm version: 4.64.0\n",
-      "lmdb version: 1.3.0\n",
-      "psutil version: 5.9.1\n",
-      "pandas version: 1.3.5\n",
-      "einops version: 0.4.1\n",
-      "transformers version: 4.20.1\n",
-      "mlflow version: 1.27.0\n",
-      "pynrrd version: 0.4.3\n",
-      "\n",
-      "For details about installing the optional dependencies, please visit:\n",
-      "    https://docs.monai.io/en/latest/installation.html#installing-the-recommended-dependencies\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "import glob\n",
-    "import logging\n",
-    "import os\n",
-    "import shutil\n",
-    "import sys\n",
-    "import tempfile\n",
-    "\n",
-    "import catalyst.dl\n",
-    "import matplotlib.pyplot as plt\n",
-    "import nibabel as nib\n",
-    "import numpy as np\n",
-    "from monai.config import print_config\n",
-    "from monai.data import Dataset, create_test_image_3d, list_data_collate, decollate_batch, DataLoader\n",
-    "from monai.inferers import sliding_window_inference\n",
-    "from monai.losses import DiceLoss\n",
-    "from monai.metrics import DiceMetric\n",
-    "from monai.networks.nets import UNet\n",
-    "from monai.transforms import (\n",
-    "    Activations,\n",
-    "    EnsureChannelFirstd,\n",
-    "    AsDiscrete,\n",
-    "    Compose,\n",
-    "    LoadImaged,\n",
-    "    RandCropByPosNegLabeld,\n",
-    "    RandRotate90d,\n",
-    "    ScaleIntensityd,\n",
-    ")\n",
-    "from monai.utils import first\n",
-    "\n",
-    "import torch\n",
-    "\n",
-    "print_config()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Setup data directory\n",
-    "\n",
-    "You can specify a directory with the `MONAI_DATA_DIRECTORY` environment variable.  \n",
-    "This allows you to save results and reuse downloads.  \n",
-    "If not specified a temporary directory will be used."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "/workspace/data/medical\n"
-     ]
-    }
-   ],
-   "source": [
-    "directory = os.environ.get(\"MONAI_DATA_DIRECTORY\")\n",
-    "root_dir = tempfile.mkdtemp() if directory is None else directory\n",
-    "print(root_dir)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Setup logging"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 272
-    },
-    "colab_type": "code",
-    "id": "r1P0mVvymvsY",
-    "outputId": "930a36c6-bbb7-49a6-9520-8335df0b9164"
-   },
-   "outputs": [],
-   "source": [
-    "logging.basicConfig(stream=sys.stdout, level=logging.INFO)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "XJpDdAo1-ZF4"
-   },
-   "source": [
-    "# [MONAI](https://github.com/Project-MONAI/MONAI) components"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "G2Wo7P7EBRdQ"
-   },
-   "source": [
-    "## Prepare synthetic data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 34
-    },
-    "colab_type": "code",
-    "id": "eq3RgOT2BclX",
-    "outputId": "3a5039ac-bb44-443a-bdc3-38fca80cc6eb"
-   },
-   "outputs": [],
-   "source": [
-    "for i in range(40):\n",
-    "    im, seg = create_test_image_3d(128, 128, 128, num_seg_classes=1, channel_dim=-1)\n",
-    "\n",
-    "    n = nib.Nifti1Image(im, np.eye(4))\n",
-    "    nib.save(n, os.path.join(root_dir, f\"img{i}.nii.gz\"))\n",
-    "\n",
-    "    n = nib.Nifti1Image(seg, np.eye(4))\n",
-    "    nib.save(n, os.path.join(root_dir, f\"seg{i}.nii.gz\"))\n",
-    "\n",
-    "images = sorted(glob.glob(os.path.join(root_dir, \"img*.nii.gz\")))\n",
-    "segs = sorted(glob.glob(os.path.join(root_dir, \"seg*.nii.gz\")))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "WX6w-86XBjeh"
-   },
-   "source": [
-    "## Prepare transforms and datasets"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 34
-    },
-    "colab_type": "code",
-    "id": "CjeN90W-feaM",
-    "outputId": "2493d49b-8457-4139-ae7a-3c5fa5bd9087",
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "train_files = [{\"img\": img, \"seg\": seg} for img, seg in zip(images[:20], segs[:20])]\n",
-    "val_files = [{\"img\": img, \"seg\": seg} for img, seg in zip(images[-20:], segs[-20:])]\n",
-    "\n",
-    "# define transforms for image and segmentation\n",
-    "train_transforms = Compose(\n",
-    "    [\n",
-    "        LoadImaged(keys=[\"img\", \"seg\"]),\n",
-    "        EnsureChannelFirstd(keys=[\"img\", \"seg\"]),\n",
-    "        ScaleIntensityd(keys=[\"img\", \"seg\"]),\n",
-    "        RandCropByPosNegLabeld(\n",
-    "            keys=[\"img\", \"seg\"],\n",
-    "            label_key=\"seg\",\n",
-    "            spatial_size=[96, 96, 96],\n",
-    "            pos=1,\n",
-    "            neg=1,\n",
-    "            num_samples=4,\n",
-    "        ),\n",
-    "        RandRotate90d(keys=[\"img\", \"seg\"], prob=0.5, spatial_axes=[0, 2]),\n",
-    "    ]\n",
-    ")\n",
-    "val_transforms = Compose(\n",
-    "    [\n",
-    "        LoadImaged(keys=[\"img\", \"seg\"]),\n",
-    "        EnsureChannelFirstd(keys=[\"img\", \"seg\"]),\n",
-    "        ScaleIntensityd(keys=[\"img\", \"seg\"]),\n",
-    "    ]\n",
-    ")\n",
-    "\n",
-    "# define dataset, data loader\n",
-    "check_ds = Dataset(data=train_files, transform=train_transforms)\n",
-    "# use batch_size=2 to load images and use RandCropByPosNegLabeld to generate 2 x 4 images for network training\n",
-    "check_loader = DataLoader(check_ds, batch_size=2, num_workers=4, collate_fn=list_data_collate)\n",
-    "check_data = first(check_loader)\n",
-    "print(check_data[\"img\"].shape, check_data[\"seg\"].shape)\n",
-    "\n",
-    "# create a training data loader\n",
-    "train_ds = Dataset(data=train_files, transform=train_transforms)\n",
-    "# use batch_size=2 to load images and use RandCropByPosNegLabeld to generate 2 x 4 images for network training\n",
-    "train_loader = DataLoader(\n",
-    "    train_ds,\n",
-    "    batch_size=2,\n",
-    "    shuffle=True,\n",
-    "    num_workers=4,\n",
-    "    collate_fn=list_data_collate,\n",
-    "    pin_memory=torch.cuda.is_available(),\n",
-    ")\n",
-    "# create a validation data loader\n",
-    "val_ds = Dataset(data=val_files, transform=val_transforms)\n",
-    "val_loader = DataLoader(val_ds, batch_size=1, num_workers=4, collate_fn=list_data_collate)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "BB8EGm5OBuIR"
-   },
-   "source": [
-    "## Prepare model, optimizer and metrics"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "gIh_W821Bvdd"
-   },
-   "outputs": [],
-   "source": [
-    "# create UNet, DiceLoss and Adam optimizer\n",
-    "# device = torch.device(\"cuda:0\")  # you don't need device, because Catalyst uses autoscaling\n",
-    "model = UNet(\n",
-    "    spatial_dims=3,\n",
-    "    in_channels=1,\n",
-    "    out_channels=1,\n",
-    "    channels=(16, 32, 64, 128, 256),\n",
-    "    strides=(2, 2, 2, 2),\n",
-    "    num_res_units=2,\n",
-    ")\n",
-    "loss_function = DiceLoss(sigmoid=True)\n",
-    "optimizer = torch.optim.Adam(model.parameters(), 1e-3)\n",
-    "\n",
-    "dice_metric = DiceMetric(include_background=True, reduction=\"mean\")\n",
-    "post_trans = Compose([Activations(sigmoid=True), AsDiscrete(threshold=0.5)])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "BSHNgJ2e8908"
-   },
-   "source": [
-    "# [Catalyst](https://github.com/catalyst-team/catalyst) experiment"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "oO8ijD62CCvm"
-   },
-   "source": [
-    "## Setup Runner"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "wtiY6gC1CCb6"
-   },
-   "outputs": [],
-   "source": [
-    "class MonaiSupervisedRunner(catalyst.dl.SupervisedRunner):\n",
-    "    def forward(self, batch):\n",
-    "        if self.is_train_loader:\n",
-    "            output = {self.output_key: self.model(batch[self.input_key])}\n",
-    "        elif self.is_valid_loader:\n",
-    "            roi_size = (96, 96, 96)\n",
-    "            sw_batch_size = 4\n",
-    "            output = {\n",
-    "                self.output_key: sliding_window_inference(batch[self.input_key], roi_size, sw_batch_size, self.model)\n",
-    "            }\n",
-    "        elif self.is_infer_loader:\n",
-    "            roi_size = (96, 96, 96)\n",
-    "            sw_batch_size = 4\n",
-    "            batch = self._batch2device(batch, self.device)\n",
-    "            output = {\n",
-    "                self.output_key: sliding_window_inference(batch[self.input_key], roi_size, sw_batch_size, self.model)\n",
-    "            }\n",
-    "            output = {**output, **batch}\n",
-    "        return output"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "OVBQ44E3CJst"
-   },
-   "source": [
-    "## Run experiment"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# define metric function to match MONAI API\n",
-    "def get_metric(y_pred, y):\n",
-    "    y_pred = [post_trans(i) for i in decollate_batch(y_pred)]\n",
-    "    dice_metric(y_pred=y_pred, y=y)\n",
-    "    metric = dice_metric.aggregate().item()\n",
-    "    dice_metric.reset()\n",
-    "    return metric"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 819
-    },
-    "colab_type": "code",
-    "id": "NogjkLJaf-1U",
-    "outputId": "d96f2f00-42f1-4863-ccfb-1a1facbbe652"
-   },
-   "outputs": [],
-   "source": [
-    "max_epochs = 50\n",
-    "val_interval = 2\n",
-    "log_dir = os.path.join(root_dir, \"logs\")\n",
-    "runner = MonaiSupervisedRunner(\n",
-    "    input_key=\"img\", input_target_key=\"seg\", output_key=\"logits\"\n",
-    ")  # you can also specify `device` here\n",
-    "runner.train(\n",
-    "    loaders={\"train\": train_loader, \"valid\": val_loader},\n",
-    "    model=model,\n",
-    "    criterion=loss_function,\n",
-    "    optimizer=optimizer,\n",
-    "    num_epochs=max_epochs,\n",
-    "    logdir=log_dir,\n",
-    "    main_metric=\"dice_metric\",\n",
-    "    minimize_metric=False,\n",
-    "    verbose=False,\n",
-    "    timeit=True,  # let's use minimal logs, but with time checkers\n",
-    "    callbacks={\n",
-    "        \"loss\": catalyst.dl.CriterionCallback(input_key=\"seg\", output_key=\"logits\"),\n",
-    "        \"periodic_valid\": catalyst.dl.PeriodicLoaderCallback(valid=val_interval),\n",
-    "        \"dice_metric\": catalyst.dl.MetricCallback(\n",
-    "            prefix=\"dice_metric\",\n",
-    "            metric_fn=lambda y_pred, y: get_metric(y_pred, y),\n",
-    "            input_key=\"seg\",\n",
-    "            output_key=\"logits\",\n",
-    "        ),\n",
-    "    },\n",
-    "    load_best_on_end=True,  # user-friendly API :)\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "ugpGCkyS83e0"
-   },
-   "source": [
-    "# Tensorboard logs"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%load_ext tensorboard\n",
-    "%tensorboard --logdir=$log_dir"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "Z1RDmcUa8tQy"
-   },
-   "source": [
-    "# Best model performance visualisation"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 1000
-    },
-    "colab_type": "code",
-    "id": "nHYP-ltLqzoC",
-    "outputId": "5a75dac9-8f25-459f-cddc-cc8b9dfc4767"
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 648x216 with 3 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 648x216 with 3 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 648x216 with 3 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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/Fxd7y+n/xB1HLc8pSKLta4Ws/UNHNnR7noMNRO30VtcP6PjnkXT/Rw5m+aqI708LjyjXokULzjjjcLeo8ePH06xZs2O840j79+9nzZrD0zcsXbqUXbv8rfdTU1M57bTTuO6666wLuIGSkpK45ppr2L59O19//XWdvWJU7HO1y2PXoBMOPX9j2OP0TG34N7qN7jKuWT3h0POWC7bi2boNgKTsbHZd+RPWX/0sEPmio6YNVzxPr41jyZ+ddigmFV88551OWV4qe04mkGcHNbzoAGidnMn6q587avmE7b15v0l/NlxhbSPSxto4eAqd946mKz0jXnxoG48o1apVKzIyMujXrx9TpkyxbLtjxozh888/x+fzkZOTwyOPPGLZtkM1adIkvvrqKyuvfGgbjyjgap+PLzuTokG5rLjbupPqKY+O5fiP9gBQ3jmHz56dbNm2Q3XKo2PJm1GId0exJdvTNh7OS+7ZHSNCyj/3Mrfr+06HY5uO/x3JSQ8U49m0JaztaOPSGJKS4m/BPHXqVC655JKI7WfPnj2sWuWvaqMhB2bMmMHMmTOtavOhhYeDJC0NgIq5x/PZyW86HI19Or86mq4T/mdJIz0tPJyVlJ7OSwXzbbl1F40Gr70IhuwKa1A9LTxiSHFxMenp6bUO2BUJPp+PhQsX2rKv+ixevJj777/fik1p4eGgl7Z+QesEPWGfv/oSXBeE900RtPBwUnLLFry3cr7TYTju1bKmvNCtY8jvP1bhEe7stMoiWVlZbNmyxdaiA/xtLQYMGBDWWB1KASS3yOXRTYsStugASNLvUDGr+OYBPLppEY8ufcfpUOKeFh5RoEuXLrz77rs0b97c1qLjIJfLxcknn0xGRobt+w7Ws2dP7r33XkdjUKGR03ty6rxd9Eq1v0V+NHmq8ytUfhD6t0R1mKtdHl2XpB0afTaSNj7Yn9+Pm0mv1PRGNXyOZz9tUkSHryPzf6Ffcx101VVXMWDAAFq2bMlpp53maCzZ2dl06NABt9tNaWkpO3bssD2GrKwsOnXqZPt+VeiKxw5g/1mVtMot5cE2K50Ox3Enpmbw244f8wJafIRDzjiZLfd4mXv823SffiOYI4sPsyONLrcvsmRfG//en99d8h7XZJdYsr140Tw5g8eO/4Qr6Gf5trXwqCEtLY1zzjnn0POlS5dSUhKZhBwwYAAjR46MyLZD0bJlSwBExJHCA6BJkyacf/75zJ+v91hDlZSdzY8jTz70vN1bP+DZuDki+9p/ViWFg/4dkW3Hqs4pO9l+yzCOe9Ke4dXjScXlfSltn0xpdw8b+04GkigcOPWo9RZUJnHrptG0eSq8/+Pimwfw1yumc3WWduevTYokU3T7APKe+R++A9YNyqeFR0CzZs1o0aIFTZs25Y47Dg/y8sQTT1BQUEBZWZmlH8ZdunQ59EEfbVJSUsjIyKDCgRkMmzZtyrhx47TwCIGrfT5VHVuxv00q30w43H31ZNdY2ixpQerOcryrvrdsf3J6T1rlxuTswxF1eloqX0x4nCuetP6bYlw782Ty7ijg844f17vqwCY+PpkwkatW/pbkz75p/HTvInjP7c1rdz5C55TGjceRSNIkhZXjn+GimYPxFW23bLtaeOAfSGvIkCEMHz78qNduvfVWABYuXMjEiRM5YEHVl5WVxYsvvkjv3r3D3lYktGjRgvT0dFasWIHX28g/aIs4VfjEqqTsbL4fl0fhtUcPUPTtbf4i5OyVl5NzbXO8FlzBS26Ry2kvrOSvrb8Ne1vxKrlVK7y7dtk6q20su3raR9zUtOEfbs2TM5g340Uu7Dmo0TktqanMm/EijR0ETFkjrMalItJMRF4TkbUiskZE+otIroh8JCIFgZ/NrQo2Uu68885ai45gZ599Nk899ZQl+1u9enXUFh0HZWZm0rdvX0f27XK5mD17dqOGhg9HPORx6exWtRYdwRb2eoNLvqh7rojGeGjZe1p0HENWUjrvffMRruPa2LK/eMjhkCU1vvGpE4341WHh9mp5EnjfGNMDOAVYA9wFzDfGdAXmB54r/LcwiouLGzXkuZNcLhdnn302SUlx3/lJ87iBJC2Nl7Z+kfC9V6JQTOfwE5u+ZETOjyG99z8r3sYMOKXB6yf37M5LBXortzFe/voNPOefbtn2Qv5EEZGmwDnACwDGmGpjzF5gKDAtsNo04NJwg4ykBx54gNNPb9h/aNu2bXnmmfCGfrZ7nI5wxXvREQ953OTTNsw86T8NWvfanELO+za8oekTeZyOaBQPOdwq2ZAsoZ1rWiZnMnba61y9ZjtXr9nOrre7HXN9I5KwI5KGqnlyBibZus+tcNp4dAR2Av8WkVOAZcAtQBtjTFFgne2APdcaQ9S2bVuaNGlYX+WUlBTy8vIAuO+++0gLDA19UHl5OQ888ECt723VqhVPPPFETBUdB/Xo0YPCwkKqq8MfCjoKxXwe/6L1N5zQwFkymyY14YZmK/iYsyh660Sy048cErmkLIP8K7+r9b2u9vnsn6zNwqJQzOdwuPzTu/uneD/j5P8w5v26J75s0WSvTVGpuoRzFnEBpwG/M8YsFpEnqXEpzxhj6hoOXURGAaPC2L8jkpKS+PWvf8155513VOFRWVnJpk2bjlj2zTffsHLlSjIyMiI690oktWzZko0bNzodRqSEnMfBOZyOs4OvNUaGJLPxwf4s6fM4TZOOLLqLPGWc++CEI5Ydv9BD2ntL8GVn8tnJs+wMVTWMZefiWMrjuvRKTeeLXm84HYY6hnAKj23ANmPM4sDz1/An+w4RaWuMKRKRtkCt0zUaYyYDk8HZuVrWrFlDVlZWg9pdiAgZGRn87ne/q/X1Jk2aHPXaO++8Q2pqKm3btrUkXqfk5OTg8Xhwu91Oh2K1kPM4OIdzJNexHJ605nxO7j2VM9NSGrR+VlI660Y8Cxx9pa+tKyvw2mEndfsVuU36Un5cshXhKutZdi52Mo9V4gj5Br4xZjuwVUS6BxadD6wG5gIHu4gMB+aEFWGEPfbYY3z3Xe2XlmtKSkoiOzu7Udu/+OKLeeaZZ7jvvvvweDyhhBgVunfvTtOmTW3dZ25uLsnJkf2wi4c8zrt8Fbd9/8uIbX/1gJdZ+I/nWX6vdVPbJwpP+9aHZuuNlHjI4Y8rj6fKxN2XGlWHcFsO/g6YLiIrgVOBB4GHgJ+KSAFwQeB5VPN6vRGfGt7j8VBYWBgVU9DHin/+85907tzZjl3FfB57fUl4jc/pMFQN77z+b6r/7yd27Cqmc/iFbh15uTTf6TCUTcJqKWaMWQHUNnVzTM2r/NBDD7F+/XpuvPHGOtdJTU0lMzO8ltDGGNauXUu3bt0i/k0+HgwbNozS0siPjBkPedz0ovWc+ODNrBv+bP0rK9tc2vcSUrYti/h+4iGHVeKI776SDWSMYe7cuTz88MPk5OQc8VpmZibNmjUjMzPTsh4p69evt2QEVLt169aNE044wbb9+Xz6Db7BjKHLw6s5/S9jnI5EBXNo5F+lopn2jQs455xzGD58OMnJyUe043C5XJZ3gfV6vRQVFdGyZctGtxlxksvl0is1Uazoup5cM+Yjp8NQSqlj0sIDGDJkCJdeeildu3YF/ON1RNqBAwcoKSnBGHPUVRalGmv7LQM487pv+H2LAqdDUUqpY0r4Wy39+vXjqquucmTulPLycvbs2UN5eXgjSarEVnpdPy64fhH/yv/C6VCUCsnukf3pnFprb18VhxL+iscdd9xB+/btLd9uQ7vO7t+/n+rqatq1a3fUay6Xi9TUVKtDU3Fm5B/fatSsnkpFm/f/PFGHMY9iU0tb4yq3bjiIhC480tLSLGu/UbObbElJSaOmlP/hhx+OWpafn0/Hjh0PPU+k9hVVVVX1r6RIys4mWUKbXEtFVrG3HLT7fL2SYqidW6KaPeg0pGiFZdtL6MJj3rx5Rw17Hqrq6mpKSkos2dZBW7duZevWrYB/1NRzzz03Jud6aSyPx8Pll1/udBgx4aXV7+s3xShU5jvAr/PPAnY4HUpUS27ZgvdWzgc0hxNJQhceVti3b58tXWONMXz++ecA9OrVq0FDvCullFLRRguPEO3evRtjjC2jnh508NbN2rVrSU5OJjc3166RPW2ze/du/vSnPzkdhlIh+7AihYnDRyBYd2laKSdU+KoZeu1vSN75raXb1cKjkYwxlJaWOjpZWmVlJXC4AWs8FR9ut/uoGX6ViiV7fRnIF1p0qNjnw0fS58ux+qt1QnandblcXHXVVY1urOnz+aioqDj0we+0AwcOsH37dn78MT4aF+7YsYOPP/7Y6TBiQlJ6OlvvHUCGRH7MGdVwr5flcPd71zodRkxwtctj/a3dnA5D1WGju4yTP/xtRLadkFc80tLSmDBhQoPX93q9eDwefD4f+/fvj2BkjVddXc26devIyMgAIDs7O2Z7v2zatInp06c7HUZMkKxMVt/8DKDdraPJc1vPpcuti5wOIyZUdWnD9zfq3ELRaFV1JaPWXk+3G5dGZPsJWXg0hs/no7y8nIqKCqdDqZMxhuXLlwPQu3dvcnJySEqy/mKW1+ttVBfhxqiqqorq/2Ol6lPkKaN4fxbHOR2IUmG6df3VZA3eELHtJ+StlsbYu3dvTH0gLl++nOLiyIwA+P3337NlyxbLt+vz+Zg9ezYTJ060fNtK2eX/Xh3PcZeucToMpcLmM5EdtiGswkNEbhORVSLynYjMFJF0EekoIotFpFBEXhGRmL0WvHPnTqqrq50Oo9HWrl1LQUHszNnxwAMPMGvWLMf2H+95rCKv7+/H0PnOJY7tX3NYWaXzK6NJGRzZdoMhFx4ikgeMA/oYY34CJAPXAA8Dk4wxXYAS4CYrAnVCrE7LboyxrYtvuO644w5WrFjhWLyJkMcqsgaO/H/kvrESfJG5DVkfzWFlJfGBaeCUH6EK91aLC2giIi4gAygCzgNeC7w+Dbg0zH1Y7sCBA4wbN67OLrHGGMtHIbXb7t27KSwstGx769ato7S01LLt+Xw+7rvvPjZs2BANV5ViLo99+/bTf/xo9vmio4dVour7+zE0+XQ1Pucneoy5HFbR5ZRHxtJ//Gi6vmTdeb4uITcuNcb8ICITgS1AJfAhsAzYa4w5WC5tA/LCjtJiXq+XRYsW1XpF42CX2VifK+TAgQMUFxeTmprKCSecEPb29u3bZ0mBsHPnTj744AMAlixZ4viVmVjNY+OuJmfGItwPx+ZVuVj2bkU6t8wZAQY6T/8an0NXOg6K1RxW0aPzK6PpPr0A786d2HFGCbnwEJHmwFCgI7AXmA0MbsT7RwGjQt1/JPh8PqqqqigrK3M6FEtUVVWxefNmMjMzadGiRcjb2bNnT9i9WTZs2MCuXbvYsmULM2fODGtbVgonj4NzOJ2MSIWoosRfd/Xg851dWFdwPN3uiJ4us1aei+3M45Q9FVxa8HPe6vqBbftUh62qruT29VfhQ+g6YSneCN9eCRZOd9oLgI3GmJ0AIvIGcBbQTERcgUq7HXD0tKuAMWYyMDnw3qhokOB2u9m3b5/TYVjK4/GwcuVKBg4cGPIEc6tXrw65vUtxcTE+n4/p06ezaFH0nKyDhJzHwTmcI7lRkcPKem+VZ1FtkpnzxCByX/yKbmxzOqSaLDsX25nHvpVrqb6iBay0a48KYFlVNevdrXhyw+VkDd5AElg+Mml9wik8tgD9RCQD/+W984GlwCfAlcAsYDgwJ9wgI8XtdpOamoqIOH7JP9J8Pl/IA4vVNiaIMQa3243L5To0dHttxowZY8skemGI6Tze5zO0jM3x4qJCme8AWUnplPnqztHJZw7CW1JCLl/ZGFmjxHQOH/wdqMiomdsjH7ud1k9/SRaRG6ejPhLOB66I3Af8EvAAy4GR+O8jzgJyA8t+ZYw5ZoMJJ694zJ49m/bt21NeXh51o5JarX///qSnN+4PPCsrq9blP/74I/n5+cyZM4frr78+2hrjLjPG9GnoylbkcY7kmr5yflhBh+rqNdu5qel2R/Ydy+ZXJvNo9968vXkxl559OZ6Nm50O6ZB55jXbcxgcymMRpm9ZSMvkTHv3mwCe25vHmye1cmTfi818Ss2eWi+zhzVyqTHmz8CfayzeAJwZznaV85KSkjKb6OkAABEdSURBVA4Nw14Xn8/HtddeS7nzLfrDonmcuIzHw6V9LsKz3fqB8ewU0zkc51ebndBlxmi6P7YJ4/ECO50O5ygJP3Lp3XffzYIFC2JqdNJQffvtt+zdu7fe9VwuV4OvjJSVlcX9bapoN2vUEH61aaDTYcQsT9F2/fBTcaHfhNEMumEk3Z/ZjqdoO96d0Vd0gM7VQmFhoSW9NmJBWVlZg45TRCIy14uKjKTPl7OxtJPTYSilHOI1Pnr987ecMPdbfPv3Y1//lNDop4s6xOVykZKSErOz2yqlYlP/z29mvTs+hjFwggcv7R5ajC9G2ikmfOGRn59PZmbiNGqqrKysdSCwpKQkUlNTSUtLw+VK+AthseXMk8nPrv8WmlLRqsOzwrfVOq9vokj4T5h77rmHvLzEGdCvoKAAj8dDhw4djlheX0PSYF6vN+57AMWSq6d9pL1aGqnKuFlffbzTYaiAmTOe1l4tCSThr3iourvM1mXBggX06NEjQtEoFXnDN/2c109s7XQYSiWkhL/ikaga03NFKaWUsope8UhAKSkppKWlhfTeKVOmMGLECGsDUspG3T67ntIbmjsdhlKWWHTAy9BLRoDDkxU2hhYeCUhEQp63Zc+ePWzbFnVzVSjVYO696XjXrXc6DKUsUW5SMctWOR1Go2jhoRpszpw5LFiwwOkwlArZoFVDOe4zPe2p+PBWeRajX4+qSd4bRNt4qAZZtGgREydOZOHChU6HolRIbi3qg++pNuS8HZWzJCvVaLOKz6TTnVE7eWGdtPBQ9SoqKmLYsGFs2ODcbIZKhWNBZRLf39CF9JVfOx2KUmFbVV3Jdm8WG/a2oDl7nA6n0bTwUMfk9Xrp1KlTtE9tr1Sdqoybh3sNxFe+1ulQlAqb23gZ/sDttHjhK5pT4HQ4IdHCQ9WptLSU5s2b4/P5nA5FqZCsd5cxtuO54IvtGZSVOugXl46gxZLYu70SrN5WViLyoogUi8h3QctyReQjESkI/GweWC4i8pSIFIrIShE5LZLBq8j57rvv6Nq1a9wUHZrHiefxPZ0Yd861MdXN8Fg0hxPXRncZF501lIvOGgrL1zgdTtga0rx7KjC4xrK7gPnGmK7A/MBzgCFA18C/UcCz1oSp7DJp0iQGDhzIiBEjKC4udjocK01F8zghdHxvJOeMHcW74wbh2bzV6XCsNBXN4YRyScFgzhk7ihvG3oZn42Y8GzdjPNE+92z96r3VYoz5TEQ61Fg8FBgYeDwNWAD8PrD8P8YYAywSkWYi0tYYU2RVwCoy/vSnP1FRUcG8efP45ptvnA7HcprH8a/LjNEkVwpdPjxA0udLnQ7HcprDiadwZ0vy34q/BtGhtvFoE5TA24E2gcd5QPBXjG2BZZrsUWTVqlWsWLHiiGWTJk2irCzhpqXWPI5RT5R0YMamM45Y1u1v3+MtKXEoIsdoDseBbZ4yLv/2hqOWy7IcB6KJvLAblxpjjIiYxr5PREbhvwSobPbqq68ya9Ysp8OIKqHkcXAOp9Pw2X1V+J5+fzCd7zhyPI74aMkROivOxZrH9tviKWP8lqE0v+joHiqx2mulPqEO4bdDRNoCBH4ebAzwA5AftF67wLKjGGMmG2P6GGP6hBiDUuEKK4+DcziF0Oa+USpMlp6LNY/tVeGr5perhrPv7N1Oh2KrUAuPucDwwOPhwJyg5dcHWlT3A/bpPUUVxTSPVazTHI5hvV4ZR86QxJs3qCHdaWcCXwHdRWSbiNwEPAT8VEQKgAsCzwHeAzYAhcC/gLERidpCt956K4sXL3Y6DNs8+OCDvPbaa06HYbt4zuPX+3dn0KqhTodhmz5/HEPXe1fUv2Kcieccvr7XxfyntKXTYSibNKRXy7V1vHR+Lesa4OZwg7JTVVUVXm/i3B12u9144qA7VmPFcx579+6j2tvC6TBsk1Jp8CXgSLpxncMlJbhNYo1n2fPpsXSfsY3EOxvr7LRKKaWiwPMPX8Y9O3o5HYZtcjb58Gza4nQYjtDCA1i2bBlr18b/PA4ff/xxvA0KpgJK5x/HuB/PqH/FGPeTRcPI2VDhdBgqAppP/YpPd3RxOgxlAy08gI8++oivvortse8bYvr06WzdGlcjOaqA4x/9kvc/jP8OYif8wQ2LVjodhlIqDFp4BLjdbioq4veb1L59++Jm3hVVO1elsN4dv4PAraiqAk/itMdKRCVlGRR54jeHD1pVXUlydaOHXIkbWngEvPPOO/z97393OoyIMMYwYsQIdu9OrL7iiSb/gS8ZfscdTocREV7j464e5+Bdl3hdDxNJ/pXfce7LE5wOI6K8xseEIdeT+Vri9KasKbGaESullFIO8RofvzhxIN7S+ByRtKH0ikeQ1atXc/vttzsdhqXKysoYOXJkQnUZTmTZ//2O80aMdDoMS62qruQXF/wSU1XldCjKBl0eX0fvv0X1sCNh8Wkea+ERzO12s3nzZu6//3783eBj29atW3n44YfZuXOn06Eom/jKy0lbuJp+d47Ga2K/Tc8/9+bzm/G34l2T2N8QE4l3126Of2sTvR+Mr+JjvbuMs8ePxVRXOx2K47TwqMHtdrNs2TJmzJhBZWWl0+GEbN26dbz55ptxOcW9OjZfRQVNpy+m+8yxbInhhnoTtvfm+Sm/SOh74YnK88OPtJ2+hs6vxEcB/W5FOhe+NIGcmYsgDr7UhksLj1oYY3jllVc4EMOjIxYUFDBv3jynw1BOMYbO4xexyZPldCQhe/273rR9/Eunw1AO8ZaU0HXCUnzE9gf162U5/Hbe9XT4Q/wP2dBQWngcw86dO6mOwcti+/bto7S01OkwVBR4e29vdnnLnQ6j0ZZVVSN7Up0OQznM+AyvlrWmyridDiVkExZdSbfRXzsdRlTRwuMYxo8fz5o1a2Jq/Auv18uUKVOYOXOm06GoKLDyNMPYzZfgNrHTuLjCV83Ix26ly62LnA5FOc3n5T/d85lXmR2Tt1yqjBvj0Y/ZmvR/pB5//OMfmTNnTv0rRonbbruNTz/91OkwVBTZd/Zuur072ukwGmzoNaNo/bTeYlGHPdWlByO3nut0GI125iO30O3GpU6HEXUkGnpviIjzQRxDamoqqamp5OXl8eijjzodzlGqq6sZMWIEABUVFTF1hSZClhljbB0/PEdyTV85apLQqJGUnU1SViYVp+TzyYv/cjqcoxR7y7nhzCsA8O7chUnAGZSDzTOv2Z7DEN15nNwil7WTOrDhghedDqVBzh01iox5KxNyJmWAxWY+pWaP1PZavVc8RORFESkWke+Clj0qImtFZKWIvCkizYJeu1tECkXkexH5uTWH4Kzq6mrKysrYuHEj99xzj9PhHKG4uJi77rqLsrIyysrKtOioQ6LnsW//fjxF20n/fDXnjB3ldDhHeL0sh2tuGIenaDueou0JX3TUJdFz2Lt7Dz3+vp9BN4zktPvHOB1OrUq8FQy6YSSDbhhJxqdrErboqE9DbrVMBQbXWPYR8BNjTC9gHXA3gIicBFwD9Ay85xkRSbYsWodVV1ezevVqnnnmGdxu5xs7FRYWMnXqVAoLC50OJRZMRfMYX3k5Ge/8j57/GEuJ1/m5if5QfDJ/e3wYKfOWOR1KLJhKguewd00BqR8s5bg313Pic9E1zsdnB+Ccp8aT+sFSUj9Yim//fqdDilr1DplujPlMRDrUWPZh0NNFwJWBx0OBWcaYKmCjiBQCZwJx04/I5/Px/vvv07FjR1JSUujZsydt27a1NYZly5ZRUlLCunXrWLhwoa37jlWax4cZj4d2f/+SM35yM6lpbu49+b8My7Z3Hp9LCgZTuLMlsiyHds9pe46G0Bw+zLujmA6PlHJSr1+xtN+LZCQ52wPq1bKm3PXhNXR9VHO5IayYq+VG4JXA4zz8yX/QtsCyuPPss88CMGzYMM444wzS0tLIy4vsoW7YsAGAl19+mfXrdbIsiyVcHncethyAPz55FYUDP6Nd6h5uaro9ovv8664eeE0S5fflkf+xXuWwWELlsO/AAfKv/I7xS86hTWoplzX9H71S022NYWppazZXtWTqF2fT7bc60F1DhVV4iMi9gAeYHsJ7RwHRdbM5BNOnT2f69Ol069aN+++//9DyJk2aIFJru5oGc7vdh27p+Hw+brvttrgYyj3ahJrHwTmcTkYEIrNH11sW8SWpuH92MQP/9cSh5R1cGSRLeB3f9vkq2RWYJ+iASebzUzPB58WFFh1WsupcHIt5vP6MA6wnlZdnjOb9s54G/G0IOqZYO3helXGzzXPkPCtT7/h/pL27hG7oOB2NEXLhISIjgIuB883hT8MfgPyg1doFlh3FGDMZmBzYVsx/mq5bt45rrrnm0PMpU6bQunXrsLY5d+5cpk2bFm5o6hjCyePgHM6R3JjP4ZQPlzK2/dmHno8pKOTSzPCGXD/1nVtqDJ4UO+OJxAorz8WxnMedrlvBWPz568o7nneXvGfp9odv+jklZ+05YlkaSyzdR6IIqfAQkcHAncC5xpjgFmpzgRki8jhwPNAVErMUHDMm/FbX2kMlsjSPj+35U3rxfJhX7bpXr4jxAa+jm+Zw7Tw//MiQrmcBMGbFci7JDL0hde+/juW4ad9AFDTGjhf1Fh4iMhMYCLQUkW3An/G3nE4DPgrcTlhkjBltjFklIq8Cq/Ff9rvZmBgaMtFC0dDrRR2medx4vgo90UYTzeHG8ZX7pwp49rJLeCYt9A49bTeswVsee9MORDMdQEzFIx1ATMU0HUBMxbqwBhBTSimllLKKFh5KKaWUso0WHkoppZSyjRYeSimllLJNtDQu3QmUA7ucjiXCWhL/xwjOH2d7Y0wrO3eYQDkMzv9+7eD0MdqewwAish/43u79OsDp369dnDzOOnM4KgoPABFZ6kQrbjslwjFC4hxnTYly3IlwnIlwjLVJlOPW43SW3mpRSimllG208FBKKaWUbaKp8JjsdAA2SIRjhMQ5zpoS5bgT4TgT4RhrkyjHrcfpoKhp46GUUkqp+BdNVzyUUkopFeccLzxEZLCIfC8ihSJyl9PxWElENonItyKyQkSWBpblishHIlIQ+Nnc6TgbS0ReFJFiEfkuaFmtxyV+TwV+vytF5DTnIo+ceM1jzWHN4VinORx9Oexo4SEiycA/gSHAScC1InKSkzFFwCBjzKlBXZruAuYbY7oC8wPPY81UYHCNZXUd1xD8U3J3BUYBz9oUo20SII81hzWHY53mcBTlsNNXPM4ECo0xG4wx1cAsYKjDMUXaUGBa4PE04FIHYwmJMeYzYE+NxXUd11DgP8ZvEdBMRNraE6ltEi2PNYc1h2Od5rCDOex04ZEHbA16vi2wLF4Y4EMRWSYiowLL2hhjigKPtwNtnAnNcnUdV7z/jiG+j1FzOL5/vwfF8zFqDkfZ79fl1I4TxNnGmB9EpDXwkYisDX7RGGNEJO66FcXrcSUozWEV6zSHo4zTVzx+APKDnrcLLIsLxpgfAj+LgTfxX87ccfASV+BnsXMRWqqu44rr33FA3B6j5jAQx7/fIHF7jJrDQJT9fp0uPJYAXUWko4ikAtcAcx2OyRIikiki2QcfAz8DvsN/fMMDqw0H5jgToeXqOq65wPWBVtX9gH1BlwLjRVzmseaw5rDDMYVNczhKc9gY4+g/4EJgHbAeuNfpeCw8rk7AN4F/qw4eG9ACf2vjAmAekOt0rCEc20ygCHDjv1d4U13HBQj+1vLrgW+BPk7HH6H/k7jLY81hzeFY/6c5HJ05rCOXKqWUUso2Tt9qUUoppVQC0cJDKaWUUrbRwkMppZRSttHCQymllFK20cJDKaWUUrbRwkMppZRSttHCQymllFK20cJDKaWUUrb5/4segqW+MOjhAAAAAElFTkSuQmCC",
-      "text/plain": [
-       "<Figure size 648x216 with 3 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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eJC4Od4a1kxdaxVmnDq49eyyZ+dOX5nD4ctSogStZJ18vKzKv/6gqad68OS+++KLdYVSb0+nkjTfeIDU11e5QlM2OXnIun82ZYncY1ZbmSOLT5Z8TV08fJIllW95rxJo73rQ7jLCjhYdSSimlQkYLD6WUUkpV6Mb5i3Cf3zFo+9PCQymllLLAGWOTaf3NLXaHEbCbauzFleJ3l9BTaOGhlFJKWeBw/XgaZIb3aLt2CF4Jo5RSSqlSh689yIJ2OnxKWXrFQymllFIho4WHUkopZQG3W3AZt91hhB0tPJRSSikLZF+3mvbjR9gdRtjRPh5KKaWUBda904nP+74IpNkdSljRwkMppZQKsrxp7fnXeW/RIl6LjrK08FBKKaWCaN3Ybkzr/HfOS0ywO5SwpIVHFHA4HHTt2vWU9fXr1ycuLo6SkhIbovKfMYbFixdHXNzKfxIXx+47u5yy/kBLG4IJgmLjoteyIWQd3WN3KCpEnLUz2XldawB+HPQyWc7omWvqwpVXkbzzMMHqJquFR4SLi4sjOzubBx98sNz3jTER9wvc7Xbz5ps6sVKscCQlUdy9LUuejp6f+TFTTMbAdbjsDkSFhDOrNvn9W/nkcPQUHQCptx6lZPvGoO0v4KdaRMQpIj+JyP/zLjcTkYUikiMi/xYRvdZkEYfDQXZ2dkTOQBtONIftI3FxFHdvy9wPJtodSsTTPLaHJCay9abWLHhxvN2hRIxgPE77APCLz/ILwN+MMS2BfcCdQWhDlaN79+5adASH5rBNdt3VRYuO4NE8tsGav3dg+WPj7A4jogRUeIhINjAQ+Kd3WYDfANO9m0wCrgqkDVW+IUOGMHz48Eq3ExHS0tLw/GjC3969exkxInTPvWsO22fd2G7M+ePLdocRdJ8cTmNw+8tC2qbmsT3i59dnxYC/2x2GZQrcRxnQ/jeUbN8R1P0GesXjVeBRKO1zUhvYb4w53qlgC9AwwDZUOeLj44mPj7c7jKAzxnDs2LFQNqk5bBOT4I6qDnjHFRknrn37Qt2s5rEN6iQVkOZIsjsMS7n25gd9n34XHiJyObDLGLPEz88PFZHFIrLY3xhU1SUmJob9VY+8vDymTJkSsvaCmcPFhLRYUmHqmd1teWnMjSFtU/PYHuvePY+7zvjK7jAss+RYEb3HlP/QQqACeaqlF3CliAwAkoB04DWglojEeSvtbGBreR82xkwAJgCIiAkgDlUFcXFxuFwuXC4Xbnf4zR2Qm5vLN998w9KlS0PZbNByOF0yNYdj3CM7zuWzqT1oOPH7UDeteWyHw3EcNfEQpc8urS+uwxljrcllv694GGMeN8ZkG2OaAkOAL4wxNwFfAtd5N7sV0DmBw0RiYiIOR3hOz7N8+XLmz58f0jY1h1UwffxlNxqOCXnRoXlsk1bDFzJ5d0+7w7DEqqJCXtvQz7L9W/Fb6DHgQRHJwXOf8W0L2lABCLdbLkePHqWoqMjuMHxpDqtqWV9cgLMwvP5foXms/LC9pIAbl91BWv8NlrURlAHEjDHzgfne1xuAU4fRVGEhKSmJY8eOUVxcbHcopcaNG8fKlSttjUFzWPnLZdz8buTDNP3wB7tD0TxWAes99WFaPLzA0jbC87q7stTMmTMZO3as3WEAMHLkSNuLDhV5Wvz7Xro8eZ/dYQBw+YCbSJ+y0O4wlApYt8fuo8WjiyxvRwuPCPXRRx8xcWL1B1564403mD17NitWrOC5556zILKqGzlyJHv26FwWsartqDzajhtW7c+d8+IwWj+3nqxpK+kzdKgFkVWNy7i57LIbMKvWgdE+mbFo15AMzvzqVrvDCBqHy4Db+s6yOldLhDp8+DALFiygpKSEoVU8+b7xxhssXbqUI0eOALBp0yZeeeUVAIYNG0ZSkvXPo2/fvp0PP/wQgF27dlnengpfJTt20uyDZM4uHsbKB6o28uM5Lw4je/omSnbvBiDlq1/o8fC9APx7zMs0jrN+CvI39jdi8rMDwUD6zwu16IhhJRvzKD5U16/PtvzgXup4H+I7UtfB8kfsG/2022P34XAZMr/eTChm9tLCI4Lt37+fhQsXUrt2ba699trTbjt9+nQWLVpUWnQAFBcXl97mmDlzJhdffDEZGRmWxZubm8uXX36pt1ZUqZING2nyfjHNG97Dhuv+cdptW/z7Xlq/v6606ABwHzpE+gee+9EXt3uEv//2n1ySYl3/pUd2nMv/Jveg/gehf3pFhacGc520PHQv7qwiNlxctavQzT69izaTD+JethqAjLpn0DLbU0B/P/ivnBGigfW2lxR4+nS8/yO4XSEpOgDEhEG1ruN4BCYuLo6HHnrotNu89NJLlY7fMWTIEOrWrUtmZiaNGjUKWnxr166lsLAwlI/MLjHGdA5FQ8elS6bpJtY9fhbtHElJuGdnVfi+G8F56XZMJTMtr32rC2c2306fOut4ImtN0OK7M+98thyuxZa5jcl+3vqiY66ZHvIcBs3jQDjPbIHrzRMDsI1vOYVm8SeuwBW4j3LtGs/TzXG3QcnmLeXuZ8+sM5nc/l3OSkixLNb/FKQzYfMF7CpI44xBv1rSxkIzj4Mmv9xHvbTwUKfo3LkzgwcPLl3Oyqr4F0J5XC4X+3yGjH7llVfYsSO4Y/1XQguPGLf37h48+sgHpcvXpu7DKVXv0lbgPsqnR05cQp944xWYxaG7UqeFR+RL/Koev6t/4kmnVYXZLDinatNcHJvTlHdaTz6pcAmWJceKGDzj97T8P2ufXNHCQwVk3Lhx1ZoXZvv27YwaNcrCiCqlhYc6ybhN31LXWfU7y2/ub8fcs2tYGNHpaeGh1k3qxM/9xuHAQYojwe/9FLiPnrTce8yDlo1I6ksLDxVrtPBQEU0LD3Wc68JOzP2g+k8wAozf35CP29YJckRVc7rCQzuXKqWUUhbImXwuH5//5inrR15yE66166u0j7hvf2bgef1PWlc0OZ7Pz5oFwNdH4YU+l/PJghlccdVtOLec6HxtSlzAbsKNFh5RrGnTptx555089dRTdoeilF9Mr46kPLedwj477Q5FqWrZ8clZTD3nH3RIOHWYgp7TV7GvOIXPP+hO/b+e/raHKSmhZPvJfeScD7ej7xl3eV4fc+PcvJRL7riHxJ9XUHIs/GcY1sIjytxwww3Url0bgIyMDDp06MCIESNK3581axabNm2yKzylKrX+pR7QwHNfOrvOPj5o/hHd3z+Rw83HupEfltsVnlKVWvt2Z2Z0HFtu0QHwZJbnSZInflfCjITzq/2klPlpFWV7fST8bzGR0mdBC48o0rdvXwYMGHDKUygDBw4sfV1UVMTcuXPZsMG6CYCU8tfO+3sy9uq36Z/i+1dbKjl93yldalZ4Fy3jzsXxzU+hD1Cp05C4OLbf35VFl7xEVhXG4niu7s8suqgJPB+C4MKIDpkeBUSEFi1a8MADD1T66OvVV1/NgAEDqFevXoiiU6oKRHBd2InPHnmxTNFxqtwB/yT3XnB0aBOi4JSqnCMlhaMXn8vyR8ZVqeg4rnHqPujewcLIwo8WHlEgPj6esWPHkpiYWKXtBw4cyD333GNxVEpVnSMtjbkfTKR+FYc8z+n7DiV/O2xxVEpVnWnXgvlvv1Xtz73d+Fvu+9dHOLNqWxBVeAqo8BCRWiIyXUR+FZFfRKSHiGSKyOciss773boxuJUKAs1jFek0hyPbVakFTF022+4wQibQKx6vAf81xrQBzgF+AUYC84wxrYB53mWlwpnmsYp0msMqYvhdeIhITeAC4G0AY0yRMWY/MAiY5N1sEnBVoEGqitWrV49x46o/q2HHjh15/vkY69FUDs1j+zk6tOG3i6o/r8o7rT6k9nf6R7zmsIo0gVzxaIZnZJJ3ROQnEfmniKQCdY0x273b7AD8mzNYVcmBAweYPHlytT+3efNm/vOf/1gQUcTRPLaZbN3Fa69fV+3Pjdvbk9yxrS2IKOJoDke4BUdd9B31B7vDCJlACo84oBPwpjHmXOAwZS7lGc947OU+WiwiQ0VksYgsDiCGmFdYWMj331d/3P29e/eyeLH+0xNAHvvmcDHhP2hPuHLtzafev1ZU+3OL8puQ/qG1E11FiKCdizWP7ZFXkknmxB8q3zBKBFJ4bAG2GGMWepen40n+nSJSH8D7fVd5HzbGTDDGdLZjPgKlfPidx745HE/VnihSygJBOxdrHvvPUXCU2/N62x1GRPC78DDG7AA2i8jxa539gNXATOBW77pbgRkBRaiqZM+ePVR1wr/CwkIOHjxocUSRQfM4TLjdzCt04jLuKm2eW1zAtv3pFgcVGTSHw4Prl3XsuLj6n9vnOsLCQy2CH1AYC3Tk0vuB90UkAdgA3I6nmJkqIncCm4DBAbahKlFUVMTNN9/M9OnTSU09/cA1xhimTZvGhx9+GKLoIoLmsc3chw/zYov2NNr0HWfGnz6Hi42Li6c+QotHYufSdBVoDkcgl3Fzza83kHBxbE1jEVDhYYxZBpR3q0TnVbbB9ddfz7hx42jatGmF24waNUr7dpSheRw+7m92ARf9vJ9HMiueubP3Y8Np8b727fClORyZzvz3MFo+tLDyDaOMztUSRYwxPPbYYzidTgDOPPNMHnrooZNGKT106JBd4SlVObeL+Ze2Zn7C2QDsOb8BE//8Co/2HVK6Scb2ZbireFtRqWDY/MeejL+z/GEL7ntrWOkkb+5DhxjYaxAAA2YvZXitzRXu89y/DKP1+6twxWAua+ERpp588knOP//8SrfLy8tj6NChpcu+fTdWrFjB6NGj2bdvnyUxKnU6Oa9254LuqyrdbtG2xjS85sR2JVu3lb6ufaiA4Xt/T2LuIktiVKosZ3o6deecXAzcnvEhF5Q/0SxP3fYh/xt0ts+afAAGpf1C848fwiS7yO3/T8Bza6XPA/fhKDE0WLiBkv0HrDiEsKeFR5hITEzk6aefLl0eMGAALVpU3uGoXbt2PPfcc6XLr732Gjt37gTgyJEjrF69OvjBKlUOR0oKv77arnT56Qs+4rb0ch+kOMnP9Y4yaML9pcttx+ymZMNGAFx79pL46d6gx6pUWcUXnUfuYAeS6OLTxhOr/LkhNfYxpMY35byTRvoaJ3WWF9F6630AiIEmH/0IbhclQYo7EmnhYbMmTZrQqVMnUlJSuP/++yv/QBm1atU66XM7d+4kLy+PDRs2sHz58mCGqlS5nO1as/P8TEqShdzLqz+KboeEJHIvPzG5Vutd95G2uT4Za4/h/HJpMENV6iT5t/fAneB5vb/3UXJ/83ZQ93+w81Gcx5Kos8xN6n9iry9HRaSqj2BaGoSI/UHYoG7dutxyyy2MGjUq6PueNm0ao0ePJi8vL+j7jgBLQj0+TLpkmm4Se/344po1Ye29DVh385tB3/e5i4ZQ/3E3rtVrg77vcDfXTA95DkPs5fFLGxfQIaGCeyhB9OyeNnx763mYnyq/9RgtFpp5HDT5Ut57gU4Sp/yQlJREcnIyTz75pCVFB3iecJk0aRLJyckkJydb0oaKXY4aNXDUqMG652taUnQA/NRlCmf9K6e0LaWCQqQ0p5zlD+YadE9m/cqYjyaeyGUp9/dxzNBbLTZYt24dtWrVsrydzp07s2uX5x57RkYGJSWxfFdRBdMLKz73/qVY3r3t4Plr/aWwxtMpb2DzHphjOqS3CoyjXWs+mzPFuxS6P8o6Jiby2RrP/5cBFw2OySt5x+kVjxCKj49n+/btISk6ytqyZQvZ2dkhb1dFF0lMZPymb0NyedqXUxxMWDcPZ+uWIW1XRZeDN3bnrU//aXcYTPjvRAoGd7c7DNto4REi9evX59tvvyUtLc2W9lNTU5kxYwbdu8dusqvAOFs1p9+SPTSLtyeHG8elMfDjhRwb2MWW9lVk2/J4Tx4e/QHZcfbkr6/suDQeePZDfrPi8ClfzqzadodnOb3VEiIJCQm0bdvW1hjOOOMM7r33XgYNGlS6rqSkhKeeesrGqFSkMIkJpx1RNBSG19rMd0/ksOKuE/+XSkqcNL6++rPbqthytK6ba9PCZ46qwWkHIO3UcTymv3suR4vrUnNyjah9EkYLjxBo0qQJN954o23tFxQUlH7v0uXkvxZdLlfpWB9z5sxh714dM0GdyqOgILcAABYKSURBVNmuNWtvD/0twvJ80OxLaHZi+Zgp5twnHwADzSZvpmRTxaNFKhXuFnWaCkCX+MHkt+lJ8h5D1j+ia14iLTwslp2dzTXXXMMTTzwR0nbdbjclJSW43W7y8/Mr3M7pdPLkk08Cnsnm1q9fT35+fqw+hqvK4WzdkpybMsm5wZqnVwKVKPGsHuYZP6R90TBqr6pH8rbDuJfp4HnKq2t7EhoetjuKalnUaSp0grcP1OP93MuJnxM9c2zpOB4W++Mf/8jIkSND3u6hQ4f8Hip99uzZ/PnPf47keV10HI8gynm1O+sHj7c7jGrptPi31Lt7H66dlY+cGo50HI/giatXlxr/KWZKsy/sDsVvucUFjOhZweTCLhclO3aGNqAqON04HnrFw2Jiw/Pae/fu5fBh/6v7gQMH0rx5c6677rpK4w+HwlVZy0TgkANLO/+bZ+a05ftzEiofM0FzOKrd/c13XJVaYHcYAWkWn8bsRZ+W+968QicvtuxwYkUE5HNAhYeI/AG4CzDACuB2oD4wBagNLAFuNsYUBRhnRJo2bRqXXnqp3WH4LT09nT179lT4vsvlIjU1FbfbHcKogk/z+DTmZbOmzRuA0+5I/OLMqs3UZbMrfP+ocXFTkwvA7QphVMGnORy7+iW76Lb5RB+QAcN/T/KMH22MqHJ+Fx4i0hD4PdDWGFMoIlOBIcAA4G/GmCkiMh64EwjPm8MWczqdfl/xyMnJKbdvRqdOnYiLq/jHtnv3bo4ePepXm+B58qVmzZo0bdqUlStXEh8fX+G28fHx5Obmll71+Mtf/sJbb71V4fbhSPP49OKdLuIlMouOh2ov5asFtUhzVDzmSBowfM0vuL0jCzz11i00ePH7EEUYHJrDFRDhll/zuCxlH1DxeSwa+Ob4838bz+6X03nw+8G0ujU85zoKdByPOCBZROKAFGA78Btguvf9ScBVAbYRU1avXs3KlSvZs2cPR48ePeXr+PsVPX1SUlLi9+2PevXqUatWLeLj40lJSaFRo0aVfqZx48Y0adKEJk2a8OCDD9rSnyUINI+jUJojiYEplRfhV6Ye4arUAq5KLWD4bTPIHdMjBNEFneZwOQalbiVRorvoKKtXkoOrUguYeP67bPigo93hlMvvwsMYsxV4GcjDk+QH8FzO22+MOT429xagYaBBRqIRI0bQsmXVRlksLi5m48aNbNy4kb1795Kfn09RUflXRPfv309+fj7btm1j48aNbNu2rfS9gwcP+n3bIysri/T09NNeTalMmzZtuOmmmxgxYoTf+wg1zeOKbXqmJ4PrLbI7jJC6t9ZWhl3xGZuf7Gl3KFWmOXwqZ3o6617tFnNFh68Lk91M6TGB9S+FXyHtd+EhIhnAIDxP1DcAUoH+1fj8UBFZLCLR84yQj+HDh9OsWbNKtysqKmL37t3k5eWRl5dX5asV+/btIy8vj82bN5c+vXLgwAFcrurfq05LSyMrKyugouO4s88+m8cee4zLLrss4H2FQiB57JvDxUTfHCJjbnyPW9Ir7uMTrf4vYyOv3PY2B2+MjFF+g3kujpY8lhppbLh+fMTeJgyW8xITWHLDK+y/uQcShPN7sARyq+UiINcYs9sYUwx8BPQCankv9wFkA1vL+7AxZoIxprMdj4yFk3379pGTk+P3548dO8aqVf5NtSwiJCQk0LhxYxyO4I2en52dzUcffUTTpk1teaqnmvzOY98cjicxdBEry/VPOcasF/6K4+w2kTCTaNDOxZrH0aemI5mFL7wZVjM8B/LbJg/oLiIp4vnt0g9YDXwJXOfd5lZgRmAhRjc7H0eNi4ur8u2g6kpKSiI3N5fU1FRL9h9EmseqXFnOVD6bMwWnDZM6VpPmsKqUJCWGTREdSB+PhXg6Li3F8/iWA5gAPAY8KCI5eB7jejsIcUalnJwc1q4NfGpkt9vN119/7ddtllineawineawqorZS/6L6XmO3WEAAY7jYYx5Gni6zOoNQNdA9qtUKGkeq0inOawiSfBu7KtqWbt27WkH5/LHjh07KC4urtK2VX1cNlBffvklLVq0sLwdpazS8Yu9OM45y+4wVDW4du2h7x13c8xU7XyoQksLD5scPny4wkdm/VVUVFTlPiNOp5OkpIoHVgqWzp07k5ycbHk7Slnlubo/40rTTpeRxJGazOabS3Dor7iTHPxjAUWX2v88h/5UokxKSkpQHotVSqlIJamp5PR9J+Yfpy1rQcfp7GudYHcYWnhEm1q1apGYqH+dKaWUCk9aeFikoKDAtqdMHA5HJIyfocLcxqIsjrh1TjEVgdxu1hdH9oy00UwLD4t06dKFL774wpa2MzIyyMjIsKVtFT0+a1eLwTmD7A5DqWor2b6DYU17U+D2f8JMZR0tPGxy7rnnUr9+/aDus2fPnqSkpACeYdAbN25MdnZ2UNtQSimlAqG9EG0U7Nsh5Q177nA4aNjQMzfUjh07dJAxpZRSttIrHhZ68cUX+eSTTyp8v379+jRo0CDgdkSEdu3aVTjfitPpxOl0Urt2berUqUOdOnVYu3Ytw4YNC7jtytx8883k5eVZ3o6yRsFL2XRfdl3lG9rgiZ0daPd363O44/PDiFuz2fJ2VJAZQ99Rf2DJMe2nFG608LDQggUL+PXXXyt8PzU1lRpBmLhHRKhdu3al2yUlJZGcnExycjL5+fl89tlnAbddmU8++YSDBw9a3o6yRuLsRexem2V3GOValN+ExrPyLW+nweRfcO3Za3k7KvgyJ/7A5pJMu8MIG2d9dzNZK+zv96KFh8U2btx42tljExMTAyo+HA4Htao5idXGjRtZuXKl322q2JK2ycELe1vZHcZJphbUZP3qwK8WKhVLGr3qxPnlUrvD0MLDau+//z6PP/54hcOj16pVi9atW5OQkEBCQtUHdnE6nSQkJJCWlsbZZ59d5c/t37+ff/zjH/zpT3/C5XKxffv2Kn+2urZt22br7LsqOOr97Xtm/rkfC46GR/+gn4uOMurDm2h1/0KkuIT5hdadxuYVOsG4Ldu/st53h1qxz3XE7jCUDwmHXwwiYn8QFmvSpEmVrjJ89913uN3uCn9hH++Q2rx589JOo9Vx7bXXMmfOnNLl+Ph4CgsLcTqDO8JfQUFBUG4j+WmJMSak4wKnS6bpJv1C2WTIOc5uw2dzptgdBj0evpf0DxaULktiIjM2fEuixAe1nbySAu5ufH5Q91lVc830kOcwRG8eyxcNmd16Fk4Jz7+1i43L8lFWi42LgYPvRL5bZmk7xy008zho8st9giI8fwoxrFevXvTu3ZusrPLvq3fv3p3evXv7VXSUp7i4mISEBPLzg3ev/Ndff6VmzZpB259Sp2OOHePKRt35uSh4967H72/I3U37BG1/yl6m3zZaTbe+I7I/Lv3lcq4c8DvL2xnUsX/Iio7KVFp4iMhEEdklIit91mWKyOciss77PcO7XkTkdRHJEZGfRaSTlcFHki1btnDWWVWf4bJ169b06NHjlK/4eP//quvTpw/z588/Zb3b7eass87ip59+8nvfx82YMYPevXvjdofX5WnN48CZX3MY0NfeJ1z63XwntT4q5+TpdvF4n+t5ZnfbgNvou2oQsy7pCO7wuLV0nOZwAIyhzeg1nPNC+BQf5/5lGAN7DcJ5YxFm1TrL/m/tcR1mYK9BuII8G3ogqnLF412gf5l1I4F5xphWwDzvMsBlQCvv11DgzeCEGflcLhdbt27lsssuo6Cg8qF8nU4n8fHxp3z564orrmDVqlUVzoi7a9cu7rrrLmbOnOl3G2+++SYjR46ssD+Lzd5F8zggpqQE19r1XDBsKLkhHo7aZdyc//t7SPjhF9xHy7+yUbJpM9/8X3f6rb7S73ZafHE7CX9Mp2TLVr/3YaF30Rz2m2vfPrKnb6TTn++zOxQAkva5KcndhGvnLsv+b808nMK1w/5ASe4mCINuFcdVOoCYMeZrEWlaZvUg4ELv60nAfOAx7/r3jKeDwgIRqSUi9Y0x1vVgjCDGGL799luefvppkpKSGDRoEF27drWsvc2bNzN+/HiMMXz99deVXoVYunQpf//73/n6668Bz+O3zz77bIXbG2N49NFHS/ujfPXVV6d9fNhOmsdBYgzJn/zIwHaP4koyXHrZYl5vsMiy5j45nMZjU25GDDT56EfclVyFcH65lIKsbrTu4Pnl4k6Edb+r+Heuy7hp+85w8J6Tm84vgh9XBC3+YNIcDlzJ1m3Um3aMMxvdx9rb7KnFznz3PsQFzX/ex0lnZO//rUOvBmdcz2f3tGHqu7+h/qzvg7K/YPL3COv6JPAOoK73dUPAd6SdLd51MZ3sZU2YMAGAgwcPEhcXR6dOwb8Kmpuby7Rp03j99der9bm5c+cyd+5cAJKTk+nQoUOF27rdbv76179G8pMrmsd+avQXz8nsy4M9eezGYl6oG/x7x+8dzGL0nGtp9dQP1fpc2rSFpE3zvHbUqEHXdtdXuK3LLTR9akFY/TVYTZrD1eTas5fmoxbR9Zzr+fyc96jpSA5JuwfchVy07Faaj1qEKSmhoj8DB/94N+mpR3mg5RfcVKN648f0W30lh455Zic/9GMdGr8SfkUHBGHIdGOM8eepFBEZiucSYMx699132bt3L6NHj8bpdNKiRYuA97lx40aKioqYOnUqL7zwQkD7Kiws5Le//W3AMUUCf/LYN4eTSLEkrnDX4KXv+WJvD8Y/vJt4cXFnzR0B73NqQU3yS9J4efaVtHqkekVHWe5Dh8gYeCjgmCJBMM7FsZLHpqSEjIHreHpJb9okb6dPyjrOSrDu2He5DvPszgvJvHwtlf2Amgz2XHEb9cr1HOo/i8y4Aq5OzeftA425u+bmU57MOeIu4r2DzQBIus1F3JZ1AGSwLujHESz+Fh47j1+2E5H6wC7v+q1AI5/tsr3rTmGMmQBMgNh4nLYis2bNYtasWdSoUYMNGzaUrk9ISKhwCPSyjvrc877++uvD9nZHGAooj31zOF0yYzaHMyf+wMcT6+CsnckVy9aXrq/pSKjSI64u42avu7B0+Z+33or8sJwWBFZ0xIignotjLY9/Oa+EX6jDC+/0Z9nFY4N+9eOAu5Bjxs2Dmweyu+f+an225YML+PjBOrh7X0LDSW/zcds69N6YQx3nyddKvjiSzcdt63iXwrJv0in8LTxmArcCY7zfZ/isHyEiU4BuwIFYv6dYVYcOHaJOnTqly++//z5XXll5J7m1a9dy3nnnWRlaNNM8DiLX3nxubtSrdLngv835rsNHlX7urQONfE6cICy3JL4opTkcBGfevoQL/vAQyx8ZF9T99nnxIeq+/j1QvaLDl+Obn/hTc8/t+Aeb9ghSZPaqdAAxEfkQT+elLGAn8DTwCTAVaAxsAgYbY/LFM7rVWDw9r48AtxtjFlcaRAxf8ahIUlIScXGV14Vut5sjR3RUvjJOGXzJ6jyO1oGXAuFITUWqMhqvy4VL5/M5SXkDiIXiXBzLeSzxCTjSUj0LDuG9ZbPIcqZW+fNvH6jHR/27MPuHWQw8/yrMvgO4Cw5jimNzkrrTDSCmI5eqaKQjl6qIpiOX2s/06ojx+bW5vVcKKx84cUXk/Pvv4cKnvufZM1Zw1bpLOfpQHcySVbh7nYPj+xVhNw5MqJ2u8AjOcztKKaVUFJHvluH7W7PJ1iZ0LDwxAFn9/y7ni7hedKzbm5q5JSQt/hEAx7fhMTpoONPCQymllKpESe4m6r6+qXTZDdT49wJsm5EqgulcLUoppZQKGS08lFJKKRUyWngopZRSKmS08FBKKaVUyGjhoZRSSqmQ0cJDKaWUUiGjhYdSSimlQkYLD6WUUkqFjBYeSimllAoZLTyUUkopFTJaeCillFIqZLTwUEoppVTIVFp4iMhEEdklIit91r0kIr+KyM8i8rGI1PJ573ERyRGRNSJyqVWBK1Udmscq0mkOq2hRlSse7wL9y6z7HDjbGNMBWAs8DiAibYEhQDvvZ8aJiDNo0Srlv3fRPFaR7V00h1UUqLTwMMZ8DeSXWTfHGFPiXVwAZHtfDwKmGGOOGWNygRygaxDjVcovmscq0mkOq2gRjD4edwCfeV83BDb7vLfFu06pcKd5rCKd5rCKCHGBfFhE/giUAO/78dmhwNBA2lcqGPzNY98cTiLFgsiUqppgnYs1j1Uo+F14iMhtwOVAP2OM8a7eCjTy2Szbu+4UxpgJwATvvkx52yhltUDy2DeH0yVTc1jZIpjnYs1jFQp+3WoRkf7Ao8CVxpgjPm/NBIaISKKINANaAT8GHqZSwad5rCKd5rCKRJVe8RCRD4ELgSwR2QI8jafndCLwuYgALDDG3GuMWSUiU4HVeC77DTfGuKwKXqmq0jxWkU5zWEULOXFlzsYg9FaLCq4lxpjOoWwwXTJNN+kXyiZVFJtrpoc8h0HzWAXPQjOPgyZfyntPRy5VSimlVMho4aGUUkqpkNHCQymllFIho4WHUkoppUImXDqX7gYOA3vsjsViWUT/MYL9x9nEGFMnlA3GUA6D/T/fULD7GEOewwAicghYE+p2bWD3zzdU7DzOCnM4LAoPABFZbEcv7lCKhWOE2DnOsmLluGPhOGPhGMsTK8etx2kvvdWilFJKqZDRwkMppZRSIRNOhccEuwMIgVg4Roid4ywrVo47Fo4zFo6xPLFy3HqcNgqbPh5KKaWUin7hdMVDKaWUUlHO9sJDRPqLyBoRyRGRkXbHE0wislFEVojIMhFZ7F2XKSKfi8g67/cMu+OsLhGZKCK7RGSlz7pyj0s8Xvf+fH8WkU72RW6daM1jzWHN4UinORx+OWxr4SEiTuAN4DKgLXCDiLS1MyYL9DXGdPR5pGkkMM8Y0wqY512ONO8C/cusq+i4LsMzJXcrYCjwZohiDJkYyGPNYc3hSKc5HEY5bPcVj65AjjFmgzGmCJgCDLI5JqsNAiZ5X08CrrIxFr8YY74G8susrui4BgHvGY8FQC0RqR+aSEMm1vJYc1hzONJpDtuYw3YXHg2BzT7LW7zrooUB5ojIEhEZ6l1X1xiz3ft6B1DXntCCrqLjivafMUT3MWoOR/fP97hoPkbN4TD7+cbZ1XCMON8Ys1VEzgA+F5Fffd80xhgRibrHiqL1uGKU5rCKdJrDYcbuKx5bgUY+y9nedVHBGLPV+30X8DGey5k7j1/i8n7fZV+EQVXRcUX1z9grao9RcxiI4p+vj6g9Rs1hIMx+vnYXHouAViLSTEQSgCHATJtjCgoRSRWRGsdfA5cAK/Ec363ezW4FZtgTYdBVdFwzgVu8vaq7Awd8LgVGi6jMY81hzWGbYwqY5nCY5rAxxtYvYACwFlgP/NHueIJ4XM2B5d6vVcePDaiNp7fxOmAukGl3rH4c24fAdqAYz73COys6LkDw9JZfD6wAOtsdv0X/JlGXx5rDmsOR/qU5HJ45rCOXKqWUUipk7L7VopRSSqkYooWHUkoppUJGCw+llFJKhYwWHkoppZQKGS08lFJKKRUyWngopZRSKmS08FBKKaVUyGjhoZRSSqmQ+f/yMcF5vil++QAAAABJRU5ErkJggg==",
-      "text/plain": [
-       "<Figure size 648x216 with 3 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "for i, valid_output in enumerate(runner.predict_loader(loader=val_loader)):\n",
-    "    if i > 4:\n",
-    "        break\n",
-    "    plt.figure(\"check\", (9, 3))\n",
-    "    plt.subplot(1, 3, 1)\n",
-    "    plt.title(\"image \" + str(i))\n",
-    "    plt.imshow(valid_output[\"img\"].detach().cpu()[0, 0, :, :, 48], cmap=\"gray\")\n",
-    "    plt.subplot(1, 3, 2)\n",
-    "    plt.title(\"label \" + str(i))\n",
-    "    plt.imshow(valid_output[\"seg\"].detach().cpu()[0, 0, :, :, 48])\n",
-    "    plt.subplot(1, 3, 3)\n",
-    "    plt.title(\"output \" + str(i))\n",
-    "    logits = valid_output[\"logits\"]\n",
-    "    plt.imshow((logits[0] > 0.5).float().detach().cpu()[0, :, :, 48])\n",
-    "    plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Cleanup data directory\n",
-    "\n",
-    "Remove directory if a temporary was used."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "8iQXpWNwlUjG"
-   },
-   "outputs": [],
-   "source": [
-    "if directory is None:\n",
-    "    shutil.rmtree(root_dir)"
-   ]
-  }
- ],
- "metadata": {
-  "accelerator": "GPU",
-  "colab": {
-   "collapsed_sections": [],
-   "name": "2007.monai-catalyst.segmentation_3d.ipynb",
-   "provenance": []
-  },
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.8.13"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 1
-}
diff --git a/README.md b/README.md
index 8c878944a..cc478fae9 100644
--- a/README.md
+++ b/README.md
@@ -100,8 +100,6 @@ The example shows the flexibility of MONAI modules in a PyTorch-based program:
 - 3D UNet, Dice loss function, Mean Dice metric for 3D segmentation task.
 - Sliding window inference.
 - Deterministic training for reproducibility.
-##### [unet_segmentation_3d_catalyst](./3d_segmentation/unet_segmentation_3d_catalyst.ipynb)
-This notebook shows how MONAI may be used in conjunction with the [Catalyst](https://github.com/catalyst-team/catalyst) framework.
 ##### [unet_segmentation_3d_ignite](./3d_segmentation/unet_segmentation_3d_ignite.ipynb)
 This notebook is an end-to-end training & evaluation example of 3D segmentation based on synthetic dataset.
 The example is a PyTorch Ignite program and shows several key features of MONAI, especially with medical domain specific transforms and event handlers for profiling (logging, TensorBoard, MLFlow, etc.).