          self._side = side
          self._input_shape = input_shape
          self._config = config
          logger.debug("Initialized: %s", self.__class__.__name__,)
  
      def _reshape_for_output(self, inputs: Tensor) -> Tensor:
          """ Reshape the input for arbitrary output sizes.
  
 
</s> remove             var_x = LeakyReLU(alpha=0.1)(var_x)
 </s> add             if not self._is_dny:
                var_x = LeakyReLU(alpha=0.1)(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"], </s> remove                  activation: str = "leakyrelu",
 </s> add                  activation: Optional[str] = "leakyrelu", </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)
        self._is_dny = self._config["dec_upscale_method"].lower() == "upscale_dny"
          self._side = side
          self._input_shape = input_shape
          self._config = config
        self._is_dny = self._config["dec_upscale_method"].lower() == "upscale_dny"
          logger.debug("Initialized: %s", self.__class__.__name__,)
  
      def _reshape_for_output(self, inputs: Tensor) -> Tensor:
          """ Reshape the input for arbitrary output sizes.
  
keep keep add keep keep keep keep keep
 Phaze-A - Updates for future implementations 
plugins/train/model/phaze_a.py