def METHOD_NAME(self, data: Dict[str, Any]) -> Dict[str, Any]:
    r"""
    Building inference samples.
    step 1. Get the pillow image.
    step 2. Do some transforms for the pillow image as the image input,
        such as resize, normalize, to tensor etc.
    step 3. Tokenize the prompt as text input.
    step 4. 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 source, image, image patch mask and label data.
    """
    image = self.get_img_pil(data[self.column_map['image']])
    patch_image = self.patch_resize_transform(image)
    prompt = self.cfg.model.get('prompt', '图片上的文字是什么?')
    inputs = self.tokenize_text(prompt)
    sample = {
        'source': inputs,
        'patch_image': patch_image,
        'patch_mask': torch.tensor([True])
    }
    if 'text' in self.column_map and self.column_map['text'] in data:
        target = data[self.column_map['text']]
        sample['label'] = unicodedata2.normalize(
            'NFKC', convert(target, 'zh-hans'))
    return sample