How to use the ramp-engine.ramp_engine.aws.api.download_predictions function in ramp-engine

def collect_results(self):
        super().collect_results()
        if self.status == 'running':
            aws._wait_until_train_finished(
                self.config, self.instance.id, self.submission)
            self.status = 'finished'
        if self.status != 'finished':
            raise ValueError("Cannot collect results if worker is not"
                             "'running' or 'finished'")

        logger.info("Collecting submission '{}'".format(self.submission))
        aws.download_log(self.config, self.instance.id, self.submission)

        if aws._training_successful(
                self.config, self.instance.id, self.submission):
            _ = aws.download_predictions(  # noqa
                self.config, self.instance.id, self.submission)
            self.status = 'collected'
            exit_status, error_msg = 0, ''
        else:
            error_msg = _get_traceback(
                aws._get_log_content(self.config, self.submission))
            self.status = 'collected'
            exit_status = 1
        logger.info(repr(self))
        return exit_status, error_msg

label = _get_submission_label_by_id(config, submission_id)
    submission = get_submission_by_id(config, submission_id)
    actual_nb_folds = get_event_nb_folds(config, submission.event.name)
    if _training_successful(conf_aws, instance_id, submission_id,
                            actual_nb_folds):
        logger.info('Training of "{}" was successful'.format(
            label, instance_id))
        if conf_aws[MEMORY_PROFILING_FIELD]:
            logger.info('Download max ram usage info of "{}"'.format(label))
            download_mprof_data(conf_aws, instance_id, submission_id)
            max_ram = _get_submission_max_ram(conf_aws, submission_id)
            logger.info('Max ram usage of "{}": {}MB'.format(label, max_ram))
            set_submission_max_ram(config, submission_id, max_ram)

        logger.info('Downloading predictions of : "{}"'.format(label))
        predictions_folder_path = download_predictions(
            conf_aws, instance_id, submission_id)
        set_predictions(config, submission_id, predictions_folder_path)
        set_time(config, submission_id, predictions_folder_path)
        set_scores(config, submission_id, predictions_folder_path)
        set_submission_state(config, submission_id, 'tested')
        logger.info('Scoring "{}"'.format(label))
        score_submission(config, submission_id)
        _run_hook(config, HOOK_SUCCESSFUL_TRAINING, submission_id)
    else:
        logger.info('Training of "{}" in "{}" failed'.format(
            label, instance_id))
        set_submission_state(config, submission_id, 'training_error')
        error_msg = _get_traceback(
            _get_log_content(conf_aws, submission_id))
        set_submission_error_msg(config, submission_id, error_msg)
        _run_hook(config, HOOK_FAILED_TRAINING, submission_id)

.format(label))
                            download_mprof_data(
                                conf_aws, instance_id, submission_name
                            )
                            max_ram = _get_submission_max_ram(
                                conf_aws, submission_name
                            )
                            logger.info('Max ram usage of "{}": {}MB'
                                        .format(label, max_ram))
                            set_submission_max_ram(
                                config, submission_id, max_ram
                            )

                        logger.info('Downloading the predictions of "{}"'
                                    .format(label))
                        path = download_predictions(
                            conf_aws, instance_id, submission_name)
                        set_predictions(config, submission_id, path)
                        set_time(config, submission_id, path)
                        set_scores(config, submission_id, path)
                        set_submission_state(config, submission_id, 'tested')
                    else:
                        logger.info('Training of "{}" failed'.format(label))
                        set_submission_state(
                            config, submission_id, 'training_error')
                        error_msg = _get_traceback(
                            _get_log_content(conf_aws, submission_name)
                        )
                        set_submission_error_msg(
                            config, submission_id, error_msg)
                        _run_hook(config, HOOK_FAILED_TRAINING, submission_id)
                    # training finished, so terminate the instance