def METHOD_NAME(example, tokenizer, max_length=512, is_test=False):
    # The original data is processed into a format that can be read in by the model,
    # enocded_ Inputs is a dict that contains inputs_ids、token_type_ids、etc.
    encoded_inputs = tokenizer(text=example["text"], max_length=max_length)
    # input_ids：After the text is segmented into tokens, the corresponding token id in the vocabulary.
    input_ids = encoded_inputs["input_ids"]
    # # token_type_ids：Does the current token belong to sentence 1 or sentence 2, that is, the segment ids.
    pinyin_ids = encoded_inputs["pinyin_ids"]
    label = np.array([example["label"]], dtype="int64")
    # return encoded_inputs
    return input_ids, pinyin_ids, label