          var_x = self.blocks.res_block(var_x, 256, kernel_initializer=self.kernel_initializer)
          var_x = self.blocks.upscale(var_x, 128, res_block_follows=True)
          var_x = self.blocks.res_block(var_x, 128, kernel_initializer=self.kernel_initializer)
          var_x = self.blocks.upscale(var_x, 64)
          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, 512)
 
</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")
          var_x = self.blocks.res_block(var_x, 256, kernel_initializer=self.kernel_initializer)
          var_x = self.blocks.upscale(var_x, 128, res_block_follows=True)
          var_x = self.blocks.res_block(var_x, 128, kernel_initializer=self.kernel_initializer)
          var_x = self.blocks.upscale(var_x, 64)
        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, 512)
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/dfaker.py