              for _ in range(self._config["dec_res_blocks"]):
                  var_x = ResidualBlock(filters)(var_x)
          else:
              var_x = self._normalization(var_x)
              var_x = LeakyReLU(alpha=0.1)(var_x)
          return var_x
  
      def _normalization(self, inputs: Tensor) -> Tensor:
          """ Add a normalization layer if requested.
  
 
</s> remove             var_x = Conv2DBlock(filters, activation="leakyrelu", name=f"fs_enc_convblk_{i}")(var_x)
 </s> add             name = f"fs_{'dny_' if self._is_alt else ''}enc"
            var_x = Conv2DBlock(filters,
                                kernel_size=self._kernel_size,
                                strides=self._strides,
                                relu_alpha=self._relu_alpha,
                                name=f"{name}_convblk_{i}")(var_x) </s> remove def _get_upscale_layer(method: str,
 </s> add def _get_upscale_layer(method: Literal["resize_images", "subpixel", "upscale_dny", "upscale_fast",
                                       "upscale_hybrid", "upsample2d"],
            if not self._is_dny:
                var_x = LeakyReLU(alpha=0.1)(var_x)
              for _ in range(self._config["dec_res_blocks"]):
                  var_x = ResidualBlock(filters)(var_x)
          else:
              var_x = self._normalization(var_x)
            if not self._is_dny:
                var_x = LeakyReLU(alpha=0.1)(var_x)
          return var_x
  
      def _normalization(self, inputs: Tensor) -> Tensor:
          """ Add a normalization layer if requested.
  
keep keep keep keep replace keep keep keep keep keep
 Phaze-A - Updates for future implementations 
plugins/train/model/phaze_a.py