                  var_x = ResidualBlock(filters)(var_x)
          else:
              var_x = self._normalization(var_x)
              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.
  
 
</s> remove     def _normalization(self, inputs: Tensor) -> Tensor:
 </s> add     def _normalization(self, inputs: tf.Tensor) -> tf.Tensor: </s> remove             var_x = LeakyReLU(alpha=0.2)(var_x)
 </s> add             var_x = kl.LeakyReLU(alpha=0.2)(var_x) </s> remove             var_x = GaussianNoise(1.0)(var_x)
 </s> add             var_x = kl.GaussianNoise(1.0)(var_x) </s> remove             var_x = LeakyReLU(alpha=0.1)(var_x)
 </s> add             var_x = kl.LeakyReLU(alpha=0.1)(var_x) </s> remove                        inputs: Tensor,
 </s> add                        inputs: tf.Tensor,
                var_x = kl.LeakyReLU(alpha=0.1)(var_x)
                  var_x = ResidualBlock(filters)(var_x)
          else:
              var_x = self._normalization(var_x)
              if not self._is_dny:
                var_x = kl.LeakyReLU(alpha=0.1)(var_x)
          return var_x
  
      def _normalization(self, inputs: Tensor) -> Tensor:
          """ Add a normalization layer if requested.
  
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