          else:
              var_x = SpatialDropout2D(0.25)(var_x)
          var_x = self.blocks.upscale(var_x, decoder_complexity // 2, **kwargs)
          var_x = self.blocks.upscale(var_x, decoder_complexity // 4, **kwargs)
          var_x = self.blocks.conv2d(var_x, 3, kernel_size=5, padding="same", activation="sigmoid")
          outputs = [var_x]
  
          if self.config.get("mask_type", None):
              var_y = input_
              var_y = self.blocks.upscale(var_y, decoder_complexity)
 
</s> remove         var_x = self.blocks.conv2d(var_x, 3, kernel_size=5, padding="same", activation="sigmoid")
 </s> add         var_x = self.blocks.conv2d(var_x, 3,
                                   kernel_size=5,
                                   padding="same",
                                   activation="sigmoid",
                                   name="face_out") </s> remove         var_x = self.blocks.conv2d(var_x, 3, kernel_size=5, padding="same", activation="sigmoid")
 </s> add         var_x = self.blocks.conv2d(var_x, 3,
                                   kernel_size=5,
                                   padding="same",
                                   activation="sigmoid",
                                   name="face_out") </s> remove         var_x = self.blocks.conv2d(var_x, 3, kernel_size=5, padding="same", activation="sigmoid")
 </s> add         var_x = self.blocks.conv2d(var_x, 3,
                                   kernel_size=5,
                                   padding="same",
                                   activation="sigmoid",
                                   name="face_out") </s> remove         var_x = self.blocks.conv2d(var_x, 3, kernel_size=5, padding="same", activation="sigmoid")
 </s> add         var_x = self.blocks.conv2d(var_x, 3,
                                   kernel_size=5,
                                   padding="same",
                                   activation="sigmoid",
                                   name="face_out")
 </s> remove         var_x = self.blocks.conv2d(var_x, 3, kernel_size=5, padding="same", activation="sigmoid")
 </s> add         var_x = self.blocks.conv2d(var_x, 3,
                                   kernel_size=5,
                                   padding="same",
                                   activation="sigmoid",
                                   name="face_out")

        var_x = self.blocks.conv2d(var_x, 3,
                                   kernel_size=5,
                                   padding="same",
                                   activation="sigmoid",
                                   name="face_out")
          else:
              var_x = SpatialDropout2D(0.25)(var_x)
          var_x = self.blocks.upscale(var_x, decoder_complexity // 2, **kwargs)
          var_x = self.blocks.upscale(var_x, decoder_complexity // 4, **kwargs)
        var_x = self.blocks.conv2d(var_x, 3,
                                   kernel_size=5,
                                   padding="same",
                                   activation="sigmoid",
                                   name="face_out")
          outputs = [var_x]
  
          if self.config.get("mask_type", None):
              var_y = input_
              var_y = self.blocks.upscale(var_y, decoder_complexity)
keep keep keep keep replace keep keep keep keep keep
 Model Structure Changes

Standardize inputs for AutoEncoders
Name outputs from AutoEncoders
Name layers in nn_blocks.py 
plugins/train/model/unbalanced.py