def METHOD_NAME():
    parser = PdArgumentParser((ModelArguments, DataArguments, TrainingArguments))
    model_args, data_args, training_args = parser.parse_args_into_dataclasses()
    paddle.set_device(training_args.device)
    if paddle.distributed.get_world_size() > 1:
        paddle.distributed.init_parallel_env()
    set_seed(training_args.seed)
    data_dir = data_args.data_path
    train_path = os.path.join(data_dir, "train.tsv")
    dev_path = os.path.join(data_dir, "dev.tsv")
    test_path = os.path.join(data_dir, "test.tsv")
    train_ds, dev_ds, test_ds = load_ds(datafiles=[train_path, dev_path, test_path])
    model = ChineseBertForSequenceClassification.from_pretrained("ChineseBERT-large", num_classes=2)
    tokenizer = ChineseBertTokenizer.from_pretrained("ChineseBERT-large")
    # Process the data into a data format that the model can read in.
    trans_func = partial(convert_example, tokenizer=tokenizer, max_length=model_args.max_seq_length)
    train_ds = train_ds.map(trans_func, lazy=False)
    dev_ds = dev_ds.map(trans_func, lazy=False)
    test_ds = test_ds.map(trans_func, lazy=False)
    # Form data into batch data, such as padding text sequences of different lengths into the maximum length of batch data,
    # and stack each data label together
    batchify_fn = DataCollator(tokenizer)
    criterion = paddle.nn.loss.CrossEntropyLoss()
    trainer = Trainer(
        model=model,
        args=training_args,
        train_dataset=train_ds if training_args.METHOD_NAME else None,
        eval_dataset=dev_ds if training_args.do_eval else None,
        tokenizer=tokenizer,
        data_collator=batchify_fn,
        criterion=criterion,
        compute_metrics=compute_metrics,
    )
    if training_args.METHOD_NAME:
        train_results = trainer.train(resume_from_checkpoint=training_args.resume_from_checkpoint)
        metrics = train_results.metrics
        trainer.save_model()
        trainer.log_metrics("train", metrics)
        trainer.save_metrics("train", metrics)
        trainer.save_state()
    if training_args.do_eval:
        eval_metrics = trainer.evaluate()
        trainer.log_metrics("eval", eval_metrics)