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