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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def _stop_trial(self, trial, error=False, error_msg=None, stop_logger=True):
 """Stops this trial. Stops this trial, releasing all allocating resources. If stopping the trial fails, the run will be marked as terminated in error, but no exception will be thrown. Args: error (bool):
 Whether to mark this trial as terminated in error. error_msg (str):
 Optional error message. stop_logger (bool):
 Whether to shut down the trial logger. """


        if stop_logger:
            trial.close_logger()

        if error:
            self.set_status(trial, Trial.ERROR)
        else:
            self.set_status(trial, Trial.TERMINATED)

        try:
            trial.write_error_log(error_msg)
            if hasattr(trial, "runner") and trial.runner:
                if (not error and self._reuse_actors
                        and self._cached_actor is None):
                    logger.debug("Reusing actor for {}".format(trial.runner))
                    self._cached_actor = trial.runner
                else:
                    logger.info(
                        "Destroying actor for trial {}. If your trainable is "
                        "slow to initialize, consider setting "
                        "reuse_actors=True to reduce actor creation "
                        "overheads.".format(trial))
                    trial.runner.stop.remote()
                    trial.runner.__ray_terminate__.remote()
        except Exception:
            logger.exception("Error stopping runner for Trial %s", str(trial))
            self.set_status(trial, Trial.ERROR)
        finally:
            trial.runner = None