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

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)

nb_trials += 1
                time.sleep(conf_aws.get('new_instance_check_interval', 6))

            _tag_instance_by_submission(conf_aws, instance.id, submission_name)
            _add_or_update_tag(conf_aws, instance.id, 'train_loop', '1')
            _add_or_update_tag(conf_aws, instance.id, 'event_name', event_name)
            logger.info('Launched instance "{}" for submission "{}"'.format(
                instance.id, submission))
            set_submission_state(config, submission.id, 'sent_to_training')
        # Score tested submissions
        submissions = get_submissions(config, event_name, 'tested')
        for submission_id, _ in submissions:
            label = _get_submission_label_by_id(config, submission_id)
            logger.info('Scoring submission : {}'.format(label))
            score_submission(config, submission_id)
            _run_hook(config, HOOK_SUCCESSFUL_TRAINING, submission_id)
        # Get running instances and process events
        instance_ids = list_ec2_instance_ids(conf_aws)
        for instance_id in instance_ids:
            if not _is_ready(conf_aws, instance_id):
                continue
            tags = _get_tags(conf_aws, instance_id)
            # Filter instances that were not launched
            # by the training loop API
            # if 'submission_id' not in tags:  # no longer added to tags
            #     continue
            if tags.get('event_name') != event_name:
                continue
            if 'train_loop' not in tags:
                continue
            # Process each instance
            submission_name = tags['Name']