          The shape tuple for the input tensor
      config: dict
          The model configuration options
      """
      def __init__(self, input_shape: tuple[int, ...], config: dict) -> None:
          self.input_shape = input_shape
          self._config = config
          self._input_shape = input_shape
  
      @property
 
</s> remove     def __call__(self) -> keras.models.Model:
 </s> add     def __call__(self) -> tf.keras.models.Model: </s> remove         self.input_shape = self._get_input_shape()
 </s> add         self.input_shape: tuple[int, int, int] = self._get_input_shape() </s> remove         self._inputs = [Input(shape=shape) for shape in input_shapes]
 </s> add         self._inputs = [kl.Input(shape=shape) for shape in input_shapes] </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 __init__(self, input_shape: tuple[int, int, int], config: dict) -> None:
          The shape tuple for the input tensor
      config: dict
          The model configuration options
      """
    def __init__(self, input_shape: tuple[int, int, int], config: dict) -> None:
          self.input_shape = input_shape
          self._config = config
          self._input_shape = input_shape
  
      @property
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