fix: restore deprecated CompressionModel(entropy_bottleneck_channels)

Some external projects that use CompressAI as an importable library may
depend on `CompressionModel` to create the `entropy_bottleneck`. Thus,
we restore this legacy behavior (but discourage its use via a warning).

```
DeprecationWarning: The entropy_bottleneck_channels parameter is deprecated. Create an entropy_bottleneck in your model directly instead:

class YourModel(CompressionModel):
    def __init__(self):
        super().__init__()
        self.entropy_bottleneck = EntropyBottleneck(entropy_bottleneck_channels)
```

compressai/models/base.py

@@ -28,6 +28,7 @@
 # ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
 
 import math
+import warnings
 
 from typing import cast
 
@@ -66,6 +67,30 @@ class CompressionModel(nn.Module):
     EntropyBottleneck or GaussianConditional modules.
     """
 
+    def __init__(self, entropy_bottleneck_channels=None, init_weights=None):
+        super().__init__()
+
+        if entropy_bottleneck_channels is not None:
+            warnings.warn(
+                "The entropy_bottleneck_channels parameter is deprecated. "
+                "Create an entropy_bottleneck in your model directly instead:\n\n"
+                "class YourModel(CompressionModel):\n"
+                "    def __init__(self):\n"
+                "        super().__init__()\n"
+                "        self.entropy_bottleneck = "
+                "EntropyBottleneck(entropy_bottleneck_channels)\n",
+                DeprecationWarning,
+                stacklevel=2,
+            )
+            self.entropy_bottleneck = EntropyBottleneck(entropy_bottleneck_channels)
+
+        if init_weights is not None:
+            warnings.warn(
+                "The init_weights parameter was removed as it was never functional.",
+                DeprecationWarning,
+                stacklevel=2,
+            )
+
     def load_state_dict(self, state_dict, strict=True):
         for name, module in self.named_modules():
             if not any(x.startswith(name) for x in state_dict.keys()):