  
          self._validate_encoder_architecture()
          self.config["freeze_layers"] = self._select_freeze_layers()
  
          self.input_shape = self._get_input_shape()
          self.color_order = _MODEL_MAPPING[self.config["enc_architecture"]].color_order
  
      def build(self) -> None:
          """ Build the model and assign to :attr:`model`.
  
 
</s> remove     def __init__(self, input_shape: tuple[int, ...], config: dict) -> None:
 </s> add     def __init__(self, input_shape: tuple[int, int, int], config: dict) -> None: </s> remove         inputs = Input(shape=self._input_shape)
 </s> add         inputs = kl.Input(shape=self._input_shape) </s> remove         autoencoder = KModel(inputs, outputs, name=self.model_name)
 </s> add         autoencoder = keras.models.Model(inputs, outputs, name=self.model_name) </s> remove     def _update_dropouts(self, model: keras.models.Model) -> keras.models.Model:
 </s> add     def _update_dropouts(self, model: tf.keras.models.Model) -> tf.keras.models.Model: </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:
        self.input_shape: tuple[int, int, int] = self._get_input_shape()
  
          self._validate_encoder_architecture()
          self.config["freeze_layers"] = self._select_freeze_layers()
  
        self.input_shape: tuple[int, int, int] = self._get_input_shape()
          self.color_order = _MODEL_MAPPING[self.config["enc_architecture"]].color_order
  
      def build(self) -> None:
          """ Build the model and assign to :attr:`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