def METHOD_NAME(self, data, tokenizer, label_map, batch_size=1):
    """
    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 `se_len`(sequence length).
        tokenizer(obj:`PretrainedTokenizer`): This tokenizer inherits from :class:`~paddlenlp.transformers.PretrainedTokenizer`
            which contains most of the methods. Users should refer to the superclass for more information regarding methods.
        label_map(obj:`dict`): The label id (key) to label str (value) map.
        batch_size(obj:`int`, defaults to 1): The number of batch.
    Returns:
        results(obj:`dict`): All the predictions labels.
    """
    examples = []
    for text in data:
        encoded_inputs = convert_example(
            text, tokenizer, label_list=label_map.values(), max_seq_len=self.max_seq_len, is_test=True
        )
        examples.append(encoded_inputs)
    # Separates data into some batches.
    batches = [examples[idx : idx + batch_size] for idx in range(0, len(examples), batch_size)]
    data_collator = DataCollatorWithPadding(tokenizer, padding=True, return_tensors="np")
    results = []
    for raw_batch in batches:
        batch = data_collator(raw_batch)
        input_ids, token_type_ids = batch["input_ids"], batch["token_type_ids"]
        self.input_handles[0].copy_from_cpu(input_ids)
        self.input_handles[1].copy_from_cpu(token_type_ids)
        self.predictor.run()
        logits = self.output_handle.copy_to_cpu()
        probs = softmax(logits, axis=1)
        idx = np.argmax(probs, axis=1)
        idx = idx.tolist()
        labels = [label_map[i] for i in idx]
        results.extend(labels)
    return results