          if arch.startswith("efficientnet_v2"):
              kwargs["include_preprocessing"] = False
          return model, kwargs
  
      def __call__(self) -> keras.models.Model:
          """ Create the Phaze-A Encoder Model.
  
          Returns
          -------
          :class:`keras.models.Model`
 
</s> remove     def __call__(self) -> keras.models.Model:
 </s> add     def __call__(self) -> tf.keras.models.Model: </s> remove     def __call__(self) -> keras.models.Model:
 </s> add     def __call__(self) -> tf.keras.models.Model: </s> remove     def __call__(self) -> keras.models.Model:
 </s> add     def __call__(self) -> tf.keras.models.Model: </s> remove         input_ = Input(shape=self._input_shape)
 </s> add         input_ = kl.Input(shape=self._input_shape) </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:
    def __call__(self) -> tf.keras.models.Model:
          if arch.startswith("efficientnet_v2"):
              kwargs["include_preprocessing"] = False
          return model, kwargs
  
    def __call__(self) -> tf.keras.models.Model:
          """ Create the Phaze-A Encoder Model.
  
          Returns
          -------
          :class:`keras.models.Model`
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