      _filters: List[int] = []
  
      def __init__(self,
                   side: Literal["a", "b", "both", "shared"],
                   input_shape: Tuple[int, int, int],
                   config: dict,
                   layer_indicies: Optional[Tuple[int, int]] = None) -> None:
          logger.debug("Initializing: %s (side: %s, input_shape: %s, layer_indicies: %s)",
                       self.__class__.__name__, side, input_shape, layer_indicies)
          self._side = side
 
</s> remove         logger.debug("Initializing: %s (side: %s, input_shape: %s, layer_indicies: %s)",
                     self.__class__.__name__, side, input_shape, layer_indicies)
 </s> add         logger.debug("Initializing: %s (side: %s, layer_indicies: %s)",
                     self.__class__.__name__, side, layer_indicies) </s> remove         self._input_shape = input_shape
 </s> add  </s> remove     def __init__(self, input_shape: Tuple[int, int, int], config: dict) -> None:
 </s> add     def __init__(self, input_shape: Tuple[int, ...], config: dict) -> None: </s> remove     input_shape: tuple
        The shape tuple for the input to the decoder.
 </s> add  </s> remove     def __call__(self, inputs: Optional[Tensor] = None) -> Tensor:
        """ Decoder Network.
 </s> add     def __call__(self, inputs: Union[Tensor, List[Tensor]]) -> Union[Tensor, List[Tensor]]:
        """ Upscale Network.
      _filters: List[int] = []
  
      def __init__(self,
                   side: Literal["a", "b", "both", "shared"],

                   config: dict,
                   layer_indicies: Optional[Tuple[int, int]] = None) -> None:
          logger.debug("Initializing: %s (side: %s, input_shape: %s, layer_indicies: %s)",
                       self.__class__.__name__, side, input_shape, layer_indicies)
          self._side = side
keep keep keep keep replace keep keep keep keep keep
 Bugfix: Phaze-A Fix learn mask for upscales in FC 
plugins/train/model/phaze_a.py