          arch = self._config["enc_architecture"]
          model = _MODEL_MAPPING.get(arch)
          model["kwargs"] = self._model_kwargs.get(arch, {})
          if arch.startswith("efficientnet_v2"):
              model["kwargs"]["include_preprocessing"] = False
          return model
  
      def __call__(self):
          """ Create the Phaze-A Encoder Model.
  
          Returns
 
</s> remove         model = _MODEL_MAPPING.get(arch)
        model["kwargs"] = self._model_kwargs.get(arch, {})
 </s> add         model = _MODEL_MAPPING[arch]
        kwargs = self._model_kwargs.get(arch, {}) </s> remove     def __call__(self):
 </s> add     def __call__(self) -> keras.models.Model: </s> remove     def _selected_model(self):
        """ dict: The selected encoder model options dictionary """
 </s> add     def _selected_model(self) -> Tuple[_EncoderInfo, dict]:
        """ tuple(dict, :class:`_EncoderInfo`): The selected encoder model and it's associated
        keyword arguments """ </s> remove     def __init__(self, config):
 </s> add     def __init__(self, config: dict) -> None: </s> remove         if self._selected_model.get("keras_name"):
            kwargs = self._selected_model["kwargs"]
 </s> add         model, kwargs = self._selected_model
        if model.keras_name:
            kwargs["include_preprocessing"] = False
        return model, kwargs
          arch = self._config["enc_architecture"]
          model = _MODEL_MAPPING.get(arch)
          model["kwargs"] = self._model_kwargs.get(arch, {})
          if arch.startswith("efficientnet_v2"):
            kwargs["include_preprocessing"] = False
        return model, kwargs
            kwargs["include_preprocessing"] = False
        return model, kwargs
  
      def __call__(self):
          """ Create the Phaze-A Encoder Model.
  
          Returns
keep keep keep keep replace replace keep keep keep keep keep
 Model updates
- Increase model summary width
- Phaze A updates
  - Update some min/max values
  - Add Decoder Filter Slope Mode
  - Add additional arguments for Upsampling2D
  - Adjust upsampling method for multiple upsamples in FC layers
  - Typing 
plugins/train/model/phaze_a.py