def METHOD_NAME(model, data, label_map, batch_size=1, pad_token_id=0):
    """
    Predicts the data labels.
    Args:
        model (obj:`paddle.nn.Layer`): A model to classify texts.
        data (obj:`List(Example)`): The processed data whose each element is a Example (numedtuple) object.
            A Example object contains `text`(word_ids) and `seq_len`(sequence length).
        label_map(obj:`dict`): The label id (key) to label str (value) map.
        batch_size(obj:`int`, defaults to 1): The number of batch.
        pad_token_id(obj:`int`, optional, defaults to 0): The pad token index.
    Returns:
        results(obj:`dict`): All the predictions labels.
    """
    # Separates data into some batches.
    batches = [data[idx : idx + batch_size] for idx in range(0, len(data), batch_size)]
    batchify_fn = lambda samples, fn=Tuple(
        Pad(axis=0, pad_val=pad_token_id),  # query_ids
        Pad(axis=0, pad_val=pad_token_id),  # title_ids
        Stack(dtype="int64"),  # query_seq_lens
        Stack(dtype="int64"),  # title_seq_lens
    ): [data for data in fn(samples)]
    results = []
    model.eval()
    for batch in batches:
        query_ids, title_ids, query_seq_lens, title_seq_lens = batchify_fn(batch)
        query_ids = paddle.to_tensor(query_ids)
        title_ids = paddle.to_tensor(title_ids)
        query_seq_lens = paddle.to_tensor(query_seq_lens)
        title_seq_lens = paddle.to_tensor(title_seq_lens)
        logits = model(query_ids, title_ids, query_seq_lens, title_seq_lens)
        probs = F.softmax(logits, axis=1)
        idx = paddle.argmax(probs, axis=1).numpy()
        idx = idx.tolist()
        labels = [label_map[i] for i in idx]
        results.extend(labels)
    return results