              should be ``None`` otherwise it should be a Tensor
  
          Returns
          -------
          :class:`keras.models.Model`
              The Decoder model
          """
          inputs = Input(shape=self._input_shape) if inputs is None else inputs
          var_x = inputs
          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         inputs = Input(shape=self._input_shape) if inputs is None else inputs
        var_x = inputs
 </s> add  </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     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. </s> remove         var_x = inputs
 </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)
              should be ``None`` otherwise it should be a Tensor
  
          Returns
          -------
         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)
         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)
          """
          inputs = Input(shape=self._input_shape) if inputs is None else inputs
          var_x = inputs
          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
keep keep keep keep replace replace keep keep keep keep keep
 Bugfix: Phaze-A Fix learn mask for upscales in FC 
plugins/train/model/phaze_a.py