def METHOD_NAME():
    parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
    infer_opts(parser)
    parser.add_argument("--labels_num", type=int, required=True,
                        help="Number of prediction labels.")
    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.")
    args = parser.parse_args()
    # Load the hyperparameters from the config file.
    args = load_hyperparam(args)
    # Build tokenizer.
    args.tokenizer = str2tokenizer[args.tokenizer](args)
    # Build classification model and load parameters.
    args.soft_targets, args.soft_alpha = False, False
    model = SiameseClassifier(args)
    model = load_model(model, args.load_model_path)
    # For simplicity, we use DataParallel wrapper to use multiple GPUs.
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model = model.to(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_a = torch.LongTensor([example[0][0] for example in dataset])
    src_b = torch.LongTensor([example[0][1] for example in dataset])
    seg_a = torch.LongTensor([example[1][0] for example in dataset])
    seg_b = torch.LongTensor([example[1][1] for example in dataset])
    batch_size = args.batch_size
    instances_num = src_a.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 i, (src_batch, seg_batch) in enumerate(batch_loader(batch_size, (src_a, src_b), (seg_a, seg_b))):
            src_a_batch, src_b_batch = src_batch
            seg_a_batch, seg_b_batch = seg_batch
            src_a_batch = src_a_batch.to(device)
            src_b_batch = src_b_batch.to(device)
            seg_a_batch = seg_a_batch.to(device)
            seg_b_batch = seg_b_batch.to(device)
            with torch.no_grad():
                _, logits = model((src_a_batch, src_b_batch), None, (seg_a_batch, seg_b_batch))
            pred = torch.argmax(logits, dim=1)
            pred = pred.cpu().numpy().tolist()
            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")