              else:
                  self._validate_input_shape()
                  inputs = self._get_inputs()
                  self._model = self.build_model(inputs)
              if not self._is_predict:
                  self._compile_model()
              self._output_summary()
  
      def _update_legacy_models(self):
          """ Load weights from legacy split models into new unified model, archiving old model files
 
</s> remove         self._model.summary(print_fn=lambda x: logger.verbose("%s", x))
 </s> add         if hasattr(self._args, "summary") and self._args.summary:
            print_fn = None  # Print straight to stdout
        else:
            print_fn = lambda x: logger.verbose("%s", x)  # print to logger
        self._model.summary(print_fn=print_fn) </s> remove                 layer.summary(print_fn=lambda x: logger.verbose("%s", x))
 </s> add                 layer.summary(print_fn=print_fn) </s> remove         self.trainer_name = self._args.trainer
 </s> add 
            if not is_summary and not self._is_predict:
              else:
                  self._validate_input_shape()
                  inputs = self._get_inputs()
                  self._model = self.build_model(inputs)
            if not is_summary and not self._is_predict:
                  self._compile_model()
              self._output_summary()
  
      def _update_legacy_models(self):
          """ Load weights from legacy split models into new unified model, archiving old model files
keep keep keep keep replace keep keep keep keep keep
 train - Add option to output model summary and exit 
plugins/train/model/_base.py