              if not self._is_dny:
                  var_x = LeakyReLU(alpha=0.1)(var_x)
          return var_x
  
      def _normalization(self, inputs: Tensor) -> Tensor:
          """ Add a normalization layer if requested.
  
          Parameters
          ----------
          inputs: tensor
 
</s> remove                 var_x = LeakyReLU(alpha=0.1)(var_x)
 </s> add                 var_x = kl.LeakyReLU(alpha=0.1)(var_x) </s> remove     def __call__(self, inputs: Tensor) -> Tensor:
 </s> add     def __call__(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 _dny_entry(self, inputs: Tensor) -> Tensor:
 </s> add     def _dny_entry(self, inputs: tf.Tensor) -> tf.Tensor: </s> remove     def _do_upsampling(self, inputs: Tensor) -> Tensor:
 </s> add     def _do_upsampling(self, inputs: tf.Tensor) -> tf.Tensor:
    def _normalization(self, inputs: tf.Tensor) -> tf.Tensor:
              if not self._is_dny:
                  var_x = LeakyReLU(alpha=0.1)(var_x)
          return var_x
  
    def _normalization(self, inputs: tf.Tensor) -> tf.Tensor:
          """ Add a normalization layer if requested.
  
          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