def METHOD_NAME(cls, predictor, tokenizer, data, parameters):
    max_seq_len = 128
    batch_size = 1
    return_attention_mask = False
    is_split_into_words = False
    if "max_seq_len" in parameters:
        max_seq_len = parameters["max_seq_len"]
    if "batch_size" in parameters:
        batch_size = parameters["batch_size"]
    if "return_attention_mask" in parameters:
        return_attention_mask = parameters["return_attention_mask"]
    if "is_split_into_words" in parameters:
        is_split_into_words = parameters["is_split_into_words"]
    text = None
    if "text" in data:
        text = data["text"]
    if text is None:
        return {}
    if isinstance(text, str):
        text = [text]
    has_pair = False
    if "text_pair" in data and data["text_pair"] is not None:
        text_pair = data["text_pair"]
        if isinstance(text_pair, str):
            text_pair = [text_pair]
        if len(text) != len(text_pair):
            raise ValueError("The length of text and text_pair must be same.")
        has_pair = True
    # Get the result of tokenizer
    pad = True
    if len(text) == 1:
        pad = False
    examples = []
    if has_pair:
        tokenizer_result = tokenizer(
            text=text,
            text_pair=text_pair,
            max_length=max_seq_len,
            truncation=True,
            return_attention_mask=return_attention_mask,
            is_split_into_words=is_split_into_words,
            padding=pad,
        )
    else:
        tokenizer_result = tokenizer(
            text=text,
            max_length=max_seq_len,
            truncation=True,
            return_attention_mask=return_attention_mask,
            is_split_into_words=is_split_into_words,
            padding=pad,
        )
    examples = []
    for input_ids, token_type_ids in zip(tokenizer_result["input_ids"], tokenizer_result["token_type_ids"]):
        examples.append((input_ids, token_type_ids))
    # Separates data into some batches.
    batches = [examples[i : i + batch_size] for i in range(0, len(examples), batch_size)]
    batchify_fn = lambda samples, fn=Tuple(
        Pad(axis=0, pad_val=tokenizer.pad_token_id, dtype="int64"),  # input
        Pad(axis=0, pad_val=tokenizer.pad_token_type_id, dtype="int64"),  # segment
    ): fn(samples)
    results = [[] for i in range(0, predictor._output_num)]
    for batch in batches:
        input_ids, token_type_ids = batchify_fn(batch)
        if predictor._predictor_type == "paddle_inference":
            predictor._input_handles[0].copy_from_cpu(input_ids)
            predictor._input_handles[1].copy_from_cpu(token_type_ids)
            predictor._predictor.run()
            output = [output_handle.copy_to_cpu() for output_handle in predictor._output_handles]
            for i, out in enumerate(output):
                results[i].append(out)
        else:
            output = predictor._predictor.run(None, {"input_ids": input_ids, "token_type_ids": token_type_ids})
            for i, out in enumerate(output):
                results[i].append(out)
    results_concat = []
    for i in range(0, len(results)):
        results_concat.append(np.concatenate(results[i], axis=0))
    out_dict = {"logits": results_concat[0].tolist(), "data": data}
    for i in range(1, len(results_concat)):
        out_dict[f"logits_{i}"] = results_concat[i].tolist()
    if return_attention_mask:
        out_dict["attention_mask"] = tokenizer_result["attention_mask"]
    return out_dict