def METHOD_NAME(self, data: Dict[str, Any]) -> Dict[str, Any]:
    r"""
    Building inference samples.
    step 1. Preprocessing the input text via `pre_cation` function, see more
        details from the doc of `pre_cation`.
    step 2. Uniform the unknown token, such as `<unk>` -> `unk` and `<unk>` -> `unk`.
    step 3. Get the prompt from input, concatenate with the input text, as new input.
    step 4. Tokenize the input text and generate the decoder prompt.
    step 5. Determine Whether or not to add labels to the sample.
    Args:
        data (`Dict[str, Any]`): Input data, should contains the key of `image` and `prompt`,
            the former refers the image input data, and the later refers the text input data.
    Return:
        A dict object, contains text, decoder prompt and label data.
    """
    source = super().pre_caption(
        data[self.column_map['text']], max_words=self.max_src_length)
    source = source.replace('[unk]', 'unk').replace('<unk>', 'unk')
    prompt = self.cfg.model.get(
        'prompt', ' " {} " Summarize the article with a title: ')
    text = prompt.format(source)
    inputs = self.tokenize_text(text)
    if self.prompt_type == 'none':
        decoder_prompt = self.bos_item
    elif self.prompt_type == 'prev_output':
        decoder_prompt = inputs[:-1]
    else:
        raise NotImplementedError
    sample = {
        'source': inputs,
        'decoder_prompt': decoder_prompt,
    }
    if 'summary' in self.column_map and self.column_map['summary'] in data:
        sample['label'] = data[self.column_map['summary']]
    return sample