code: def _mask_targets(target, tokenized_lens, speakers):
      # cur_idx = 0
      cur_idx = tokenized_lens[0]
      tokenized_lens = tokenized_lens[1:]
      target[:cur_idx] = IGNORE_INDEX
      for tokenized_len, speaker in zip(tokenized_lens, speakers):
          if speaker == "human":
              target[
                  cur_idx + 2 : cur_idx + tokenized_len
              ] = IGNORE_INDEX
          cur_idx += tokenized_len
  
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 10 for calling a global function with example usage: _mask_targets(target, tokenized_lens, speakers) without return types