def METHOD_NAME(model, data, label_map, batch_size=1, pad_token_id=0, vocab=None):
    """
    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
        Stack(dtype="int64"),
    ): [data for data in fn(samples)]
    model.train()
    results = []
    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)
        probs, addiational_info = model.forward_interpreter(query_ids, title_ids, query_seq_lens, title_seq_lens)
        query_emb = addiational_info["embedded"][0]
        title_emb = addiational_info["embedded"][1]
        predicted_class_probs = paddle.max(probs, axis=-1)
        predicted_class_probs = predicted_class_probs.sum()
        paddle.autograd.backward([predicted_class_probs])
        q_gradients = ((query_emb * query_emb.grad).sum(-1).detach()).abs()  # gradients: (1, seq_len)
        q_grad_output = q_gradients / q_gradients.sum(-1, keepdim=True)
        t_gradients = ((title_emb * title_emb.grad).sum(-1).detach()).abs()  # gradients: (1, seq_len)
        t_grad_output = t_gradients / t_gradients.sum(-1, keepdim=True)
        model.clear_gradients()
        for query_id, title_id in zip(query_ids.numpy().tolist(), title_ids.numpy().tolist()):
            query = [vocab._idx_to_token[idx] for idx in query_id]
            title = [vocab._idx_to_token[idx] for idx in title_id]
        results.append([q_grad_output, query, t_grad_output, title])
        print([q_grad_output, query, t_grad_output, title])
    return results