          else:
              # print to logger
              print_fn = lambda x: logger.verbose("%s", x)  # noqa
          for model in _get_all_sub_models(self._model):
              model.summary(print_fn=print_fn)
  
      def save(self):
          """ Save the model to disk.
  
          Saves the serialized model, with weights, to the folder location specified when
 
</s> remove     def _reshape_for_output(self, inputs):
 </s> add     def _reshape_for_output(self, inputs: Tensor) -> Tensor: </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: </s> remove     def build(self):
 </s> add     def build(self) -> None: </s> remove             model["kwargs"]["include_preprocessing"] = False
        return model
 </s> add             kwargs["include_preprocessing"] = False
        return model, kwargs </s> remove     def __call__(self):
 </s> add     def _do_upsampling(self, inputs: Tensor) -> Tensor:
        """ Perform the upsampling at the end of the fully connected layers.

        Parameters
        ----------
        inputs: Tensor
            The input to the upsample layers

        Returns
        -------
        Tensor
            The output from the upsample layers
        """
        upsample_filts = self._scale_filters(self._config["fc_upsample_filters"])
        upsampler = self._config["fc_upsampler"].lower()
        num_upsamples = self._config["fc_upsamples"]
        var_x = inputs
        if upsampler == "upsample2d" and num_upsamples > 1:
            upscaler = _get_upscale_layer(upsampler,
                                          upsample_filts,  # Not used but required
                                          upsamples=2 ** num_upsamples,
                                          interpolation="bilinear")
            var_x = upscaler(var_x)
        else:
            for _ in range(num_upsamples):
                upscaler = _get_upscale_layer(upsampler,
                                              upsample_filts,
                                              activation="leakyrelu")
                var_x = upscaler(var_x)
        if upsampler == "upsample2d":
            var_x = LeakyReLU(alpha=0.1)(var_x)
        return var_x

    def __call__(self) -> keras.models.Model:
            model.summary(line_length=100, print_fn=print_fn)
          else:
              # print to logger
              print_fn = lambda x: logger.verbose("%s", x)  # noqa
          for model in _get_all_sub_models(self._model):
            model.summary(line_length=100, print_fn=print_fn)
  
      def save(self):
          """ Save the model to disk.
  
          Saves the serialized model, with weights, to the folder location specified when
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/_base.py