          self._relu_alpha = 0.2 if self._is_alt else 0.1
          self._kernel_size = 3 if self._is_alt else 5
          self._strides = 1 if self._is_alt else 2
  
      def __call__(self, inputs: Tensor) -> Tensor:
          """ Call the original Faceswap Encoder
  
          Parameters
          ----------
          inputs: tensor
 
</s> remove     def _normalization(self, inputs: Tensor) -> Tensor:
 </s> add     def _normalization(self, inputs: tf.Tensor) -> tf.Tensor: </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         if model.keras_name:
 </s> add         if model.keras_name and self._config["enc_architecture"].startswith("clipv_"):
            assert model.keras_name in T.get_args(TypeModelsViT)
            kwargs["input_shape"] = self._input_shape
            kwargs["load_weights"] = self._config["enc_load_weights"]
            retval = ViT(T.cast(TypeModelsViT, model.keras_name),
                         input_size=self._input_shape[0],
                         load_weights=self._config["enc_load_weights"])()
        elif model.keras_name: </s> remove                 var_x = LeakyReLU(alpha=0.1)(var_x)
 </s> add                 var_x = kl.LeakyReLU(alpha=0.1)(var_x) </s> remove     def _do_upsampling(self, inputs: Tensor) -> Tensor:
 </s> add     def _do_upsampling(self, inputs: tf.Tensor) -> tf.Tensor:
    def __call__(self, inputs: tf.Tensor) -> tf.Tensor:
          self._relu_alpha = 0.2 if self._is_alt else 0.1
          self._kernel_size = 3 if self._is_alt else 5
          self._strides = 1 if self._is_alt else 2
  
    def __call__(self, inputs: tf.Tensor) -> tf.Tensor:
          """ Call the original Faceswap Encoder
  
          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