          """
          input_ = Input(shape=self._input_shape)
          var_x = input_
  
          scaling = self._selected_model.get("scaling")
          if scaling:
              #  Some models expect different scaling.
              logger.debug("Scaling to %s for '%s'", scaling, self._config["enc_architecture"])
              if scaling == (0, 255):
                  # models expecting inputs from 0 to 255.
 
</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: </s> remove             for _ in range(self._config["fc_upsamples"]):
                upscaler = _get_upscale_layer(self._config["fc_upsampler"].lower(),
                                              upsample_filts,
                                              activation="leakyrelu")
                var_x = upscaler(var_x)
                if self._config["fc_upsampler"].lower() == "upsample2d":
                    var_x = LeakyReLU(alpha=0.1)(var_x)
 </s> add             var_x = self._do_upsampling(var_x) </s> remove             model.summary(print_fn=print_fn)
 </s> add             model.summary(line_length=100, print_fn=print_fn) </s> remove                              self._config["dec_filter_slope"])
 </s> add                              self._config["dec_filter_slope"],
                             mode=self._config["dec_slope_mode"]) </s> remove     def _build_g_blocks(self, inputs):
 </s> add     def _build_g_blocks(
                self,
                inputs: Dict[str, List[keras.models.Model]]
            ) -> Dict[str, Union[List[keras.models.Model], keras.models.Model]]:
        scaling = self._selected_model[0].scaling
          """
          input_ = Input(shape=self._input_shape)
          var_x = input_
  
        scaling = self._selected_model[0].scaling
          if scaling:
              #  Some models expect different scaling.
              logger.debug("Scaling to %s for '%s'", scaling, self._config["enc_architecture"])
              if scaling == (0, 255):
                  # models expecting inputs from 0 to 255.
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