code: def reset_trial(self, trial, new_config, new_experiment_tag):
          
          trial.experiment_tag = new_experiment_tag
          trial.config = new_config
          trainable = trial.runner
          with warn_if_slow("reset_config"):
              reset_val = ray.get(trainable.reset_config.remote(new_config))
          return reset_val
docs: Tries to invoke `Trainable.reset_config()` to reset trial.
  
          Args:
              trial (Trial): Trial to be reset.
              new_config (dict): New configuration for Trial
                  trainable.
              new_experiment_tag (str): New experiment name
                  for trial.
  
          Returns:
              True if `reset_config` is successful else False.