def METHOD_NAME(args, verbalizer, example_keys=None):
    """
    Load fewclue datasets and convert them to the standard format of PET.
    """
    split_id = args.split_id
    splits = [f"train_{split_id}", f"dev_{split_id}", "test_public", "test"]
    if args.task_name == "cluewsc":
        train_ds, dev_ds, public_test_ds, test_ds = load_dataset("fewclue", name=args.task_name, splits=splits)
        unlabeled_ds = None
    else:
        splits.append("unlabeled")
        train_ds, dev_ds, public_test_ds, test_ds, unlabeled_ds = load_dataset(
            "fewclue", name=args.task_name, splits=splits
        )
    data_ds = [train_ds, dev_ds, public_test_ds, test_ds, unlabeled_ds]
    # Preprocess data for mask prediction task.
    if args.task_name == "chid":
        for index, sub_data_ds in enumerate(data_ds):
            data_ds[index] = convert_chid(sub_data_ds)
    elif args.task_name == "cluewsc":
        for index, sub_data_ds in enumerate(data_ds[:-1]):
            data_ds[index] = convert_cluewsc(sub_data_ds)
    elif args.task_name == "csl":
        for index, sub_data_ds in enumerate(data_ds):
            data_ds[index] = convert_csl(sub_data_ds)
    orig_key = "label"
    pop_keys = ["id"]
    if args.task_name == "tnews":
        orig_key = "label_desc"
        pop_keys = ["keywords", "label", "id"]
    elif args.task_name == "iflytek":
        orig_key = "label_des"
        pop_keys = ["id", "label"]
    elif args.task_name == "ocnli":
        pop_keys = ["level", "label0", "label1", "label2", "label3", "label4", "genre", "prem_id", "id"]
    convert_label = partial(
        convert_labels_to_ids, orig_key=orig_key, labels_to_ids=verbalizer.labels_to_ids, pop_keys=pop_keys
    )
    for index, sub_data_ds in enumerate(data_ds):
        if sub_data_ds is not None:
            data_ds[index] = sub_data_ds.map(convert_label)
    # Extend train dataset with data augmentation and pseudo-label data.
    data_ds[0] = extend_with_data_augment(
        data_ds[0], args.augment_type, args.num_augment, args.word_augment_percent, args.augment_method, example_keys
    )
    data_ds[0] = extend_with_pseudo_data(data_ds[0], args.pseudo_data_path, verbalizer.labels_to_ids)
    dev_labels = [x["label_ids"] for x in data_ds[1]]
    test_labels = [x["label_ids"] for x in data_ds[2]]
    convert_fn = partial(convert_ids_to_words, token_ids=verbalizer.token_ids[:, 0, :])
    data_ds[:3] = [x.map(convert_fn) for x in data_ds[:3]]
    return data_ds, (dev_labels, test_labels)