def METHOD_NAME(save_path, task_name, label_preds, verbalizer, labels):
    """
    Combine unsupervised data and corresponding predicted labels and
    save one example per line.
    """
    if task_name == "cluewsc":
        return None
    data_ds = load_dataset("fewclue", name=task_name, splits="unlabeled")
    preds = paddle.to_tensor(label_preds.predictions)
    preds = verbalizer.aggregate_multiple_mask(preds)
    preds = paddle.nn.functional.softmax(preds, axis=1).numpy()
    label_preds = np.argmax(preds, axis=1)
    label_probs = np.max(preds, axis=1)
    pseudo_data = []
    for index, example in enumerate(data_ds):
        example["labels"] = labels[label_preds[index]]
        example["prob"] = str(label_probs[index])
        pseudo_data.append(example)
    save_data(pseudo_data, save_path)