def METHOD_NAME(args):
    if args.seed is not None:
        paddle.seed(args.seed)
        np.random.seed(args.seed)
        random.seed(args.seed)
    env_info = get_sys_env()
    info = ['{}: {}'.format(k, v) for k, v in env_info.items()]
    info = '\n'.join(['', format('Environment Information', '-^48s')] + info +
                     ['-' * 48])
    logger.info(info)
    place = 'gpu' if env_info['Paddle compiled with cuda'] and env_info[
        'GPUs used'] else 'cpu'
    paddle.set_device(place)
    if not args.cfg:
        raise RuntimeError('No configuration file specified.')
    cfg = Config(
        args.cfg,
        learning_rate=args.learning_rate,
        iters=args.iters,
        batch_size=args.batch_size)
    # Only support for the DeepLabv3+ model
    if args.data_format == 'NHWC':
        if cfg.dic['model']['type'] != 'DeepLabV3P':
            raise ValueError(
                'The "NHWC" data format only support the DeepLabV3P model!')
        cfg.dic['model']['data_format'] = args.data_format
        cfg.dic['model']['backbone']['data_format'] = args.data_format
        loss_len = len(cfg.dic['loss']['types'])
        for i in range(loss_len):
            cfg.dic['loss']['types'][i]['data_format'] = args.data_format
    train_dataset = cfg.train_dataset
    if train_dataset is None:
        raise RuntimeError(
            'The training dataset is not specified in the configuration file.')
    elif len(train_dataset) == 0:
        raise ValueError(
            'The length of train_dataset is 0. Please check if your dataset is valid'
        )
    val_dataset = cfg.val_dataset if args.do_eval else None
    losses = cfg.loss
    msg = '\n---------------Config Information---------------\n'
    msg += str(cfg)
    msg += '------------------------------------------------'
    logger.info(msg)
    train(
        cfg.model,
        train_dataset,
        val_dataset=val_dataset,
        optimizer=cfg.optimizer,
        save_dir=args.save_dir,
        iters=cfg.iters,
        batch_size=cfg.batch_size,
        resume_model=args.resume_model,
        save_interval=args.save_interval,
        log_iters=args.log_iters,
        num_workers=args.num_workers,
        use_vdl=args.use_vdl,
        losses=losses,
        keep_checkpoint_max=args.keep_checkpoint_max,
        test_config=cfg.test_config,
        fp16=args.fp16,
        profiler_options=args.profiler_options)