def METHOD_NAME():
    parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
    infer_opts(parser)
    tokenizer_opts(parser)
    parser.add_argument("--output_logits", action="store_true", help="Write logits to output file.")
    parser.add_argument("--output_prob", action="store_true", help="Write probabilities to output file.")
    parser.add_argument("--prompt_id", type=str, default="chnsenticorp_char")
    parser.add_argument("--prompt_path", type=str, default="models/prompts.json")
    args = parser.parse_args()
    # Load the hyperparameters from the config file.
    args = load_hyperparam(args)
    # Build tokenizer.
    args.tokenizer = str2tokenizer[args.tokenizer](args)
    process_prompt_template(args)
    answer_position = [0] * len(args.tokenizer.vocab)
    for answer in args.answer_word_dict_inv:
        answer_position[int(args.tokenizer.vocab[answer])] = 1
    args.answer_position = torch.LongTensor(answer_position)
    args.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    # Build classification model and load parameters.
    model = ClozeTest(args)
    model = load_model(model, args.load_model_path)
    # For simplicity, we use DataParallel wrapper to use multiple GPUs.
    model = model.to(args.device)
    if torch.cuda.device_count() > 1:
        print("{} GPUs are available. Let's use them.".format(torch.cuda.device_count()))
        model = torch.nn.DataParallel(model)
    dataset = read_dataset(args, args.test_path)
    src = torch.LongTensor([sample[0] for sample in dataset])
    tgt = torch.LongTensor([sample[1] for sample in dataset])
    seg = torch.LongTensor([sample[2] for sample in dataset])
    batch_size = args.batch_size
    instances_num = src.size()[0]
    print("The number of prediction instances: ", instances_num)
    model.eval()
    with open(args.prediction_path, mode="w", encoding="utf-8") as f:
        f.write("label")
        if args.output_logits:
            f.write("\t" + "logits")
        if args.output_prob:
            f.write("\t" + "prob")
        f.write("\n")
        for _, (src_batch, tgt_batch, seg_batch, _) in enumerate(batch_loader(batch_size, src, tgt, seg)):
            src_batch = src_batch.to(args.device)
            tgt_batch = tgt_batch.to(args.device)
            seg_batch = seg_batch.to(args.device)
            with torch.no_grad():
                _, pred, logits = model(src_batch, tgt_batch, seg_batch)
            logits = logits[:, args.answer_position > 0]
            prob = nn.Softmax(dim=1)(logits)
            logits = logits.cpu().numpy().tolist()
            prob = prob.cpu().numpy().tolist()
            for j in range(len(pred)):
                f.write(str(pred[j]))
                if args.output_logits:
                    f.write("\t" + " ".join([str(v) for v in logits[j]]))
                if args.output_prob:
                    f.write("\t" + " ".join([str(v) for v in prob[j]]))
                f.write("\n")