          # If input is inters, shapes will be a list.
          # There will only ever be 1 input. For inters: either inter out, or concatenate of inters
          # For g-block, this only ever has one output
          input_ = input_[0] if isinstance(input_, list) else input_
          input_shape = K.int_shape(input_)[1:]
  
          if self.config["split_decoders"]:
              retval = dict(a=Decoder("a", input_shape, self.config)()(inputs["a"]),
                            b=Decoder("b", input_shape, self.config)()(inputs["b"]))
          else:
 
</s> remove         inputs: Tensor, optional
            If the input is an output from another model (such as the Fully Connected Model) this
            should be ``None`` otherwise it should be a Tensor
 </s> add         inputs: Tensor or list of tensors
            Input tensor(s) to upscale block. This will be a single tensor if learn mask is not
            selected or if this is the first call to the upscale blocks. If learn mask is selected
            and this is not the first call to upscale blocks, then this will be a list of the face
            and mask tensors. </s> remove         :class:`keras.models.Model`
            The Decoder model
 </s> add          Tensor or list of tensors
            The output of encoder blocks. Either a single tensor (if learn mask is not enabled) or
            list of tensors (if learn mask is enabled) </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.
        # If learning a mask and upscales have been placed into FC layer, then the mask will also
        # come as an input
        if self.config["learn_mask"] and self.config["dec_upscales_in_fc"]:
            input_ = input_[0]

          # If input is inters, shapes will be a list.
          # There will only ever be 1 input. For inters: either inter out, or concatenate of inters
          # For g-block, this only ever has one output
          input_ = input_[0] if isinstance(input_, list) else input_
        # If learning a mask and upscales have been placed into FC layer, then the mask will also
        # come as an input
        if self.config["learn_mask"] and self.config["dec_upscales_in_fc"]:
            input_ = input_[0]

          input_shape = K.int_shape(input_)[1:]
  
          if self.config["split_decoders"]:
              retval = dict(a=Decoder("a", input_shape, self.config)()(inputs["a"]),
                            b=Decoder("b", input_shape, self.config)()(inputs["b"]))
          else:
keep keep keep add keep keep keep keep keep keep
 Bugfix: Phaze-A Fix learn mask for upscales in FC 
plugins/train/model/phaze_a.py