code: def preprocess_multimodal(
      sources: dict,
      multimodal_cfg: dict,
      cur_token_len: int,
  ) -> Dict:
      """
      Preprocesses a given multimodal input based on specified configurations.
  
      This function modifies the 'sources' dictionary, primarily focusing on conversations. It checks if the input
      is multimodal based on 'multimodal_cfg'. If not, it returns the 'sources' unmodified. For multimodal inputs,
      it processes each conversation in 'sources'. If 'sep_image_conv_front' is set in 'multimodal_cfg', the function
      asserts the presence of 'DEFAULT_IMAGE_TOKEN' at the beginning of each conversation, removes it, and restructures
      the conversation's first turn with this token and other formatting details. Furthermore, the function replaces
      'DEFAULT_IMAGE_TOKEN' with a series of 'DEFAULT_IMAGE_PATCH_TOKEN' tokens, the count of which depends on
      'image_token_len' and 'use_im_start_end' configuration.
  
      Parameters:
      - sources (dict): A dictionary containing the source data to be processed. Each source is expected to have
        'conversations' as one of its keys.
      - multimodal_cfg (dict): A configuration dictionary specifying how the multimodal data should be processed.
        Key configurations include 'is_multimodal', 'sep_image_conv_front', and 'use_im_start_end'.
      - cur_token_len (int): The current length of image tokens, used to determine the number of patch tokens
        to replace the 'DEFAULT_IMAGE_TOKEN'.
  
      Returns:
      - dict: The modified 'sources' dictionary after applying the multimodal preprocessing.
      """
      is_multimodal = multimodal_cfg["is_multimodal"]
      image_token_len = cur_token_len
      if not is_multimodal:
          return sources
  
      for source in sources:
          conversation = source["conversations"]
          if multimodal_cfg["sep_image_conv_front"]:
              assert DEFAULT_IMAGE_TOKEN in conversation[0]["value"]
              conversation[0]["value"] = (
                  conversation[0]["value"]
                  .replace(DEFAULT_IMAGE_TOKEN, "")
                  .strip()
              )
              conversation[0]["value"] = (
                  DEFAULT_IMAGE_TOKEN
                  + conversation_lib.default_conversation.sep
                  + conversation_lib.default_conversation.roles[0]
                  + ": "
                  + conversation[0]["value"]
              )
          for turn in conversation:
              if multimodal_cfg["use_im_start_end"]:
                  replace_token = (
                      DEFAULT_IMAGE_PATCH_TOKEN * image_token_len
                  )
              else:
                  replace_token = DEFAULT_IMAGE_PATCH_TOKEN * (
                      image_token_len - 2
                  )
              replace_token = (
                  DEFAULT_IM_START_TOKEN
                  + replace_token
                  + DEFAULT_IM_END_TOKEN
              )
              turn["value"] = turn["value"].replace(
                  DEFAULT_IMAGE_TOKEN, replace_token
              )
  
      return sources
  
imports: import copy
  import json
  import logging
  import os
  import re
  import tarfile
  from dataclasses import dataclass
  from typing import Any, Dict, List, Sequence, Union
  import torch
  import torch.nn.functional
  import transformers
  from einops import rearrange
  from omegaconf import DictConfig
  from PIL import Image
  from torch.utils.data import Dataset, default_collate
  from transformers import CLIPImageProcessor
  import nemo.collections.multimodal.data.neva.conversation
  from nemo.collections.nlp.modules.common.megatron.utils import get_ltor_masks_and_position_ids
label_desc: Use image node_id 2 for calling a global function with example usage: preprocess_multimodal(sources, multimodal_cfg, cur_token_len) and returns: sources, sources