          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
              var_y = self._reshape_for_output(var_y)
  
          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         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         inputs = Input(shape=self._input_shape)
 </s> add         inputs = Input(shape=self._input_shape) if inputs is None else inputs </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         upscales = int(np.log2(self._config["output_size"] / K.int_shape(var_x)[1]))
        filters = _get_curve(self._config["dec_max_filters"],
                             self._config["dec_min_filters"],
                             upscales,
                             self._config["dec_filter_slope"],
                             mode=self._config["dec_slope_mode"])
 </s> add         if not self._filters:
            upscales = int(np.log2(self._config["output_size"] / K.int_shape(var_x)[1]))
            self._filters.extend(_get_curve(self._config["dec_max_filters"],
                                            self._config["dec_min_filters"],
                                            upscales,
                                            self._config["dec_filter_slope"],
                                            mode=self._config["dec_slope_mode"]))
            logger.debug("Generated class filters: %s", self._filters)

        filters = self._filters[start_idx: end_idx] </s> remove         upscaler = _get_upscale_layer(self._config["dec_upscale_method"].lower(), filters)
 </s> add         upscaler = _get_upscale_layer(self._config["dec_upscale_method"].lower(),
                                      filters,
                                      activation="leakyrelu",
                                      upsamples=2,
                                      interpolation="bilinear")
        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)
          inputs = Input(shape=self._input_shape)
          var_x = inputs
          var_x = self._reshape_for_output(var_x)
  
        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)
        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)
        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)
  
          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)
keep keep keep keep replace replace 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