def METHOD_NAME():
    # get config and arguments
    args, config = get_pretrain_args()
    # Fix seed and make backends deterministic
    random.seed(args.seed)
    np.random.seed(args.seed)
    torch.manual_seed(args.seed)
    if torch.cuda.is_available():
        torch.cuda.manual_seed_all(args.seed)
        torch.backends.cudnn.deterministic = True
        torch.backends.cudnn.benchmark = False
    runner = Runner(args, config)
    eval('runner.train')()
    runner.logger.close()