          self.kernel_initializer = RandomNormal(0, 0.02)
          super().__init__(*args, **kwargs)
          logger.debug("Initialized %s", self.__class__.__name__)
  
      def build_autoencoders(self):
          """ Initialize Dfaker model """
          logger.debug("Initializing model")
          inputs = [Input(shape=self.input_shape, name="face")]
          if self.config.get("mask_type", None):
              mask_shape = (self.input_shape[0] * 2, self.input_shape[1] * 2, 1)
              inputs.append(Input(shape=mask_shape, name="mask"))
  
          for side in ("a", "b"):
              decoder = self.networks["decoder_{}".format(side)].network
              output = decoder(self.networks["encoder"].network(inputs[0]))
              autoencoder = KerasModel(inputs, output)
              self.add_predictor(side, autoencoder)
          logger.debug("Initialized model")
  
      def decoder(self):
          """ Decoder Network """
          input_ = Input(shape=(8, 8, 512))
          var_x = input_
  
 
</s> remove         inputs = [Input(shape=self.input_shape, name="face")]
        if self.config.get("mask_type", None):
            mask_shape = (self.input_shape[:2] + (1, ))
            inputs.append(Input(shape=mask_shape, name="mask"))

 </s> add  </s> remove         inputs = [Input(shape=self.input_shape, name="face")]
        if self.config.get("mask_type", None):
            mask_shape = self.config["output_size"], self.config["output_size"], 1
            inputs.append(Input(shape=mask_shape, name="mask"))

 </s> add  </s> remove         inputs = [Input(shape=self.input_shape, name="face")]
        if self.config.get("mask_type", None):
            mask_shape = (self.input_shape[:2] + (1, ))
            inputs.append(Input(shape=mask_shape, name="mask"))

 </s> add  </s> remove     def build_autoencoders(self):
        """ Initialize original model """
 </s> add     def build_autoencoders(self, inputs):
        """ Initialize realface model """ </s> remove     def build_autoencoders(self):
 </s> add     def build_autoencoders(self, inputs):
          self.kernel_initializer = RandomNormal(0, 0.02)
          super().__init__(*args, **kwargs)
          logger.debug("Initialized %s", self.__class__.__name__)
  















      def decoder(self):
          """ Decoder Network """
          input_ = Input(shape=(8, 8, 512))
          var_x = input_
  
keep keep keep keep replace replace replace replace replace replace replace replace replace replace replace replace replace replace 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