          self._is_dny = self._config["dec_upscale_method"].lower() == "upscale_dny"
          self._layer_indicies = layer_indicies
          logger.debug("Initialized: %s", self.__class__.__name__,)
  
      def _reshape_for_output(self, inputs: Tensor) -> Tensor:
          """ Reshape the input for arbitrary output sizes.
  
          The number of filters in the input will have been scaled to the model output size allowing
          us to scale the dimensions to the requested output size.
  
 
</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             style = Dense(self._dense_nodes, kernel_initializer="he_normal")(style)
 </s> add             style = kl.Dense(self._dense_nodes, kernel_initializer="he_normal")(style) </s> remove         "min_max": (0, 100),
 </s> add         "min_max": (0, 200), </s> remove         inputs = Input(shape=self._input_shape)
 </s> add         inputs = kl.Input(shape=self._input_shape) </s> remove             var_x = Reshape(new_shape)(var_x)
 </s> add             var_x = kl.Reshape(new_shape)(var_x)
    def _reshape_for_output(self, inputs: tf.Tensor) -> tf.Tensor:
          self._is_dny = self._config["dec_upscale_method"].lower() == "upscale_dny"
          self._layer_indicies = layer_indicies
          logger.debug("Initialized: %s", self.__class__.__name__,)
  
    def _reshape_for_output(self, inputs: tf.Tensor) -> tf.Tensor:
          """ Reshape the input for arbitrary output sizes.
  
          The number of filters in the input will have been scaled to the model output size allowing
          us to scale the dimensions to the requested output size.
  
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