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

5) download th log
        6) set the predictions in the database
        7) score the submission
    """
    conf_aws = config[AWS_CONFIG_SECTION]
    upload_submission(conf_aws, instance_id, submission_id)
    launch_train(conf_aws, instance_id, submission_id)
    set_submission_state(config, submission_id, 'training')
    _run_hook(config, HOOK_START_TRAINING, submission_id)
    _wait_until_train_finished(conf_aws, instance_id, submission_id)
    download_log(conf_aws, instance_id, submission_id)

    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)

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

set_submission_state(config, submission_id, 'training')
                _run_hook(config, HOOK_START_TRAINING, submission_id)

            elif state == 'training':
                # in any case (successful training or not)
                # download the log
                download_log(conf_aws, instance_id, submission_name)
                if _training_finished(conf_aws, instance_id, submission_name):
                    logger.info(
                        'Training of "{}" finished, checking '
                        'if successful or not...'.format(label))
                    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_name,
                            actual_nb_folds):
                        logger.info('Training of "{}" was successful'
                                    .format(label))
                        if conf_aws.get(MEMORY_PROFILING_FIELD):
                            logger.info('Download max ram usage info of "{}"'
                                        .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'