code: def preprocess_multimodal(
      sources: Sequence[str], data_args: DataArguments
  ) -> Dict:
      is_multimodal = data_args.is_multimodal
      if not is_multimodal:
          return sources
  
      for source in sources:
          for sentence in source:
              if DEFAULT_IMAGE_TOKEN in sentence["value"]:
                  sentence["value"] = (
                      sentence["value"]
                      .replace(DEFAULT_IMAGE_TOKEN, "")
                      .strip()
                  )
                  sentence["value"] = (
                      DEFAULT_IMAGE_TOKEN + "\n" + sentence["value"]
                  )
                  sentence["value"] = sentence["value"].strip()
                  if (
                      "mmtag"
                      in conversation_lib.default_conversation.version
                  ):
                      sentence["value"] = sentence["value"].replace(
                          DEFAULT_IMAGE_TOKEN,
                          "<Image>" + DEFAULT_IMAGE_TOKEN + "</Image>",
                      )
              replace_token = DEFAULT_IMAGE_TOKEN
              if data_args.mm_use_im_start_end:
                  replace_token = (
                      DEFAULT_IM_START_TOKEN
                      + replace_token
                      + DEFAULT_IM_END_TOKEN
                  )
              sentence["value"] = sentence["value"].replace(
                  DEFAULT_IMAGE_TOKEN, replace_token
              )
  
      return sources
  
imports: import os
  import copy
  from dataclasses import dataclass, field
  import json
  import logging
  import pathlib
  from typing import Dict, Optional, Sequence, List
  import torch
  import transformers
  from llmga.llava.constants import IGNORE_INDEX, IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_TOKEN, DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN, DEFAULT_OUTPUT_START_TOKEN, DEFAULT_OUTPUT_END_TOKEN
  from torch.utils.data import Dataset
  from llmga.llava.train.llava_trainer import LLaVATrainer
  from llmga.llava import conversation
  from llmga.llava.model import *
  from llmga.llava.mm_utils import tokenizer_image_token
  from PIL import Image
  from llmga.llava.masks.make_mask import get_mask_generator
  from llmga.llava.prompt_temp import outpaint_prompt, inpaint_prompt, textextend_prompt, regen_prompt, ques_prompt, textextend_prompt2
  import random
  import numpy
  import lmdb
  import io
  import pytextrank
  from torchvision import transforms
label_desc: Use image node_id 12 for calling a global function with example usage: preprocess_multimodal(sources, data_args) and returns: sources, sources