              dim = self._config["fc_dimensions"]
              upsample_filts = self._scale_filters(self._config["fc_upsample_filters"])
  
              var_x = Reshape((dim, dim, int(self._max_nodes / (dim ** 2))))(var_x)
              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)
  
          return KerasModel(input_, var_x, name=f"fc_{self._side}")
  
  
  class GBlock():  # pylint:disable=too-few-public-methods
 
</s> remove             upsample_filts = self._scale_filters(self._config["fc_upsample_filters"])

 </s> add  </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                              self._config["dec_filter_slope"])
 </s> add                              self._config["dec_filter_slope"],
                             mode=self._config["dec_slope_mode"]) </s> remove     def _normalization(self, inputs):
 </s> add     def _normalization(self, inputs: Tensor) -> Tensor: </s> remove         scaling = self._selected_model.get("scaling")
 </s> add         scaling = self._selected_model[0].scaling
            var_x = self._do_upsampling(var_x)
              dim = self._config["fc_dimensions"]
              upsample_filts = self._scale_filters(self._config["fc_upsample_filters"])
  
              var_x = Reshape((dim, dim, int(self._max_nodes / (dim ** 2))))(var_x)
            var_x = self._do_upsampling(var_x)
            var_x = self._do_upsampling(var_x)
            var_x = self._do_upsampling(var_x)
            var_x = self._do_upsampling(var_x)
            var_x = self._do_upsampling(var_x)
            var_x = self._do_upsampling(var_x)
            var_x = self._do_upsampling(var_x)
  
          return KerasModel(input_, var_x, name=f"fc_{self._side}")
  
  
  class GBlock():  # pylint:disable=too-few-public-methods
keep keep keep keep replace replace replace replace replace 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