              The requested model plugin
          """
          logger.debug("Loading Model")
          model_dir = str(get_folder(self._args.model_dir))
          model = PluginLoader.get_model(self.trainer_name)(
              model_dir,
              self._args,
              predict=False)
          model.build()
          logger.debug("Loaded Model")
 
</s> remove             if not self._is_predict:
 </s> add             if not is_summary and not self._is_predict: </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         self.trainer_name = self._args.trainer
 </s> add 
        model = PluginLoader.get_model(self._args.trainer)(
              The requested model plugin
          """
          logger.debug("Loading Model")
          model_dir = str(get_folder(self._args.model_dir))
        model = PluginLoader.get_model(self._args.trainer)(
              model_dir,
              self._args,
              predict=False)
          model.build()
          logger.debug("Loaded Model")
keep keep keep keep replace keep keep keep keep keep
 train - Add option to output model summary and exit 
scripts/train.py