code: def preprocess_v1(
      sources: dict,
      tokenizer,
      cfg,
  ) -> Dict:
      """
      Preprocess a given set of conversational sources using vicuna v1 conversation template
  
      This function processes conversations by first ensuring the conversation starts with a 'human' role, then tokenizes the conversations, applies specific token replacements, and finally masks labels for training purposes.
  
      Parameters:
      - sources: A dictionary containing conversational data. Expected format is a dict of conversations, where each conversation is a list of messages, and each message is a dict with 'from' (role) and 'value' (message text).
      - tokenizer: A tokenizer from the Hugging Face Transformers library used for tokenizing the conversations.
      - cfg: Configuration settings which include 'add_extra_token' (bool) to determine if an extra token should be added to the tokenized output, and 'context_length' for specifying the tokenization context length.
  
      Returns:
      - Dict: A dictionary containing two keys:
          - 'tokens': A tensor of tokenized conversation data.
          - 'labels': A tensor of labels for the conversation data, used for training models. Labels are masked based on the conversation structure.
  
      Note:
      - The function includes specific token replacements (e.g., DEFAULT_IMAGE_PATCH_TOKEN, <s>, </s>) and masking techniques for labels.
      - It is designed to work with conversational data where messages alternate between a 'human' and a 'gpt' role.
      - The function asserts that each message in a conversation alternates between the defined roles and skips messages not starting with the 'human' role.
      """
      conv = conversation_lib.conv_vicuna_v1.copy()
      roles = {"human": conv.roles[0], "gpt": conv.roles[1]}
  
      # Apply prompt templates
      conversations = []
      for i, source in enumerate(sources):
          source = source["conversations"]
          if roles[source[0]["from"]] != conv.roles[0]:
              # Skip the first one if it is not from human
              source = source[1:]
  
          conv.messages = []
          for j, sentence in enumerate(source):
              role = roles[sentence["from"]]
              assert role == conv.roles[j % 2], f"{i}"
              conv.append_message(role, sentence["value"])
          conversations.append(conv.get_prompt())
  
      add_extra_token = cfg.get("add_extra_token")
      # Tokenize conversations
      tokens = tokenize(
          texts=conversations,
          tokenizer=tokenizer,
          context_length=cfg.get("context_length"),
          add_extra_token=add_extra_token,
      )
  
      # llama tricks
      tokens[tokens == 32003] = 0  # DEFAULT_IMAGE_PATCH_TOKEN
      tokens[tokens == 32006] = 1  # <s>
      tokens[tokens == 32007] = 2  # </s>
      labels = tokens.clone().detach()
  
      # Mask labels
      sep = conv.sep + conv.roles[1] + ": "
      for conversation, target in zip(conversations, labels):
          rounds = conversation.split(conv.sep2)
          cur_len = 0
          for i, rou in enumerate(rounds):
              if rou == "":
                  break
  
              parts = rou.split(sep)
              if len(parts) != 2:
                  break
              parts[0] += sep
  
              round_len = len(tokenizer.text_to_ids(rou + conv.sep2))
              instruction_len = len(tokenizer.text_to_ids(parts[0])) - 1
              if i > 0:
                  round_len -= (
                      1  # Remove extra token added by sp tokenizer
                  )
                  instruction_len -= 1
              target[cur_len : cur_len + instruction_len] = IGNORE_INDEX
  
              cur_len += round_len
          target[cur_len:] = IGNORE_INDEX
  
      if add_extra_token:
          tokens = tokens[:, :-1].contiguous()
          labels = labels[:, 1:].contiguous()
      else:
          labels = torch.roll(labels, shifts=-1, dims=-1)
          labels[:, -1] = IGNORE_INDEX
  
      return dict(
          tokens=tokens,
          labels=labels,
      )
  
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 4 for calling a global function with example usage: preprocess_v1(sources, tokenizer, cfg) and returns: dict