          logger.debug("Decoders: %s", retval)
          return retval
  
  
  def _bottleneck(inputs: Tensor, bottleneck: str, size: int, normalization: str) -> Tensor:
      """ The bottleneck fully connected layer. Can be called from Encoder or FullyConnected layers.
  
      Parameters
      ----------
      inputs: tensor
 
</s> remove     def _do_upsampling(self, inputs: Tensor) -> Tensor:
 </s> add     def _do_upsampling(self, inputs: tf.Tensor) -> tf.Tensor: </s> remove     bottleneck: str
        The type of layer to use for the bottleneck
 </s> add     bottleneck: str or ``None``
        The type of layer to use for the bottleneck. ``None`` to not use a bottleneck </s> remove     def _g_block(cls, inputs: Tensor, style: Tensor, filters: int, recursions: int = 2) -> Tensor:
 </s> add     def _g_block(cls,
                 inputs: tf.Tensor,
                 style: tf.Tensor,
                 filters: int,
                 recursions: int = 2) -> tf.Tensor: </s> remove             inputs: dict[str, keras.models.Model]) -> dict[str, list[keras.models.Model]]:
 </s> add             inputs: dict[str, tf.keras.models.Model]) -> dict[str, list[tf.keras.models.Model]]: </s> remove                         inputs: dict[str, list[keras.models.Model] | keras.models.Model]
                        ) -> dict[str, keras.models.Model]:
 </s> add                         inputs: dict[str, list[tf.keras.models.Model] | tf.keras.models.Model]
                        ) -> dict[str, tf.keras.models.Model]:
def _bottleneck(inputs: tf.Tensor, bottleneck: str, size: int, normalization: str) -> tf.Tensor:
          logger.debug("Decoders: %s", retval)
          return retval
  
  
def _bottleneck(inputs: tf.Tensor, bottleneck: str, size: int, normalization: str) -> tf.Tensor:
      """ The bottleneck fully connected layer. Can be called from Encoder or FullyConnected layers.
  
      Parameters
      ----------
      inputs: tensor
keep keep keep keep 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