                               "Current input names: {}".format(inputs))
          self.state.inputs = inputs
          logger.debug("Added input shapes: %s", self.state.inputs)
  
      def set_output_shape(self, model):
          """ Set the output shape for use in training and convert """
          logger.debug("Setting output shape")
          out = [K.int_shape(tensor)[-3:] for tensor in model.outputs]
          if not out:
              raise ValueError("No outputs found! Check your model.")
          self.output_shape = tuple(out[0])
          logger.debug("Added output shape: %s", self.output_shape)
  
      def reset_pingpong(self):
          """ Reset the models for pingpong training """
          logger.debug("Resetting models")
  
          # Clear models and graph
 
</s> remove         if not self.output_shape:
            self.set_output_shape(model)
 </s> add  </s> remove         self.build_autoencoders()
 </s> add         inputs = self.get_inputs()
        self.build_autoencoders(inputs) </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 
                               "Current input names: {}".format(inputs))
          self.state.inputs = inputs
          logger.debug("Added input shapes: %s", self.state.inputs)
  









      def reset_pingpong(self):
          """ Reset the models for pingpong training """
          logger.debug("Resetting models")
  
          # Clear models and graph
keep keep keep keep 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/_base.py