          -------
          :class:`keras.models.Model`
              The Decoder model
          """
          inputs = Input(shape=self._input_shape)
          var_x = inputs
          var_x = self._reshape_for_output(var_x)
  
          if self._config["learn_mask"]:
              var_y = inputs
 
</s> remove         if self._config["learn_mask"]:
            var_y = inputs
            var_y = self._reshape_for_output(var_y)
 </s> add         if start_idx == 0:
            var_x = self._reshape_for_output(var_x)

            if self._config["learn_mask"]:
                var_y = inputs
                var_y = self._reshape_for_output(var_y) </s> remove         var_x = self._reshape_for_output(var_x)
 </s> add         start_idx, end_idx = (0, None) if self._layer_indicies is None else self._layer_indicies
        end_idx = None if end_idx == -1 else end_idx </s> remove         if self._is_dny:
            var_x = self._dny_entry(var_x)
        if self._is_dny and self._config["learn_mask"]:
            var_y = self._dny_entry(var_y)
 </s> add             if self._is_dny:
                var_x = self._dny_entry(var_x)
            if self._is_dny and self._config["learn_mask"]:
                var_y = self._dny_entry(var_y) </s> remove     def __call__(self) -> keras.models.Model:
        """ G-Block Network.
 </s> add         return KerasModel(input_, var_x, name=f"fc_{self._side}") </s> remove         Returns
        -------
        :class:`keras.models.Model`
            The G-Block model
        """
        var_x, style = self._inputs
        for i in range(self._dense_recursions):
            style = Dense(self._dense_nodes, kernel_initializer="he_normal")(style)
            if i != self._dense_recursions - 1:  # Don't add leakyReLu to final output
                style = LeakyReLU(0.1)(style)
 </s> add 
        inputs = Input(shape=self._input_shape) if inputs is None else inputs
          -------
          :class:`keras.models.Model`
              The Decoder model
          """
        inputs = Input(shape=self._input_shape) if inputs is None else inputs
          var_x = inputs
          var_x = self._reshape_for_output(var_x)
  
          if self._config["learn_mask"]:
              var_y = inputs
keep keep keep keep replace keep keep keep keep keep
 Train updates
  - Output full model summary last
  - Fix activations in Phaze-A upscales
  - Phaze-A Add option to place some upscales in fc model 
plugins/train/model/phaze_a.py