      Parameters
      ----------
      inputs: tensor
          The input to the bottleneck layer
      bottleneck: str
          The type of layer to use for the bottleneck
      size: int
          The number of nodes for the dense layer (if selected)
      normalization: str
          The normalization method to use prior to the bottleneck layer
  
 
</s> remove             "\n\tmax_pooling: Use a Global Max Pooling 2D layer for the bottleneck."),
 </s> add             "\n\tmax_pooling: Use a Global Max Pooling 2D layer for the bottleneck."
            "\n\flatten: Don't use a bottleneck at all. Some encoders output in a size that make "
            "a bottleneck unnecessary. This option flattens the output from the encoder, with no "
            "further operations"), </s> remove def _bottleneck(inputs: Tensor, bottleneck: str, size: int, normalization: str) -> Tensor:
 </s> add def _bottleneck(inputs: tf.Tensor, bottleneck: str, size: int, normalization: str) -> tf.Tensor: </s> remove     def _reshape_for_output(self, inputs: Tensor) -> Tensor:
 </s> add     def _reshape_for_output(self, inputs: tf.Tensor) -> tf.Tensor: </s> remove         "choices": ["average_pooling", "dense", "max_pooling"],
 </s> add         "choices": ["average_pooling", "dense", "max_pooling", "flatten"], </s> remove                        interpolation: str | None = None) -> keras.layers.Layer:
 </s> add                        interpolation: str | None = None) -> tf.keras.layers.Layer:
    bottleneck: str or ``None``
        The type of layer to use for the bottleneck. ``None`` to not use a bottleneck
      Parameters
      ----------
      inputs: tensor
          The input to the bottleneck layer
    bottleneck: str or ``None``
        The type of layer to use for the bottleneck. ``None`` to not use a bottleneck
    bottleneck: str or ``None``
        The type of layer to use for the bottleneck. ``None`` to not use a bottleneck
      size: int
          The number of nodes for the dense layer (if selected)
      normalization: str
          The normalization method to use prior to the bottleneck layer
  
keep keep keep keep replace replace keep keep keep keep keep
 Updates:
    - Remove clipfaker
    - Add clip to Phaze-A
    - streamline clip to vision only
    - update lib.model.networks
    - Docs and tests update
    - Phaze-A - add option to flatten bottleneck 
plugins/train/model/phaze_a.py