code: def worker_init_fn(worker_id: int) -> None:
      """Sets the random seed for each worker.
  
      Args:
          worker_id: ID of the corresponding worker
      """
  
      if "PYTHONHASHSEED" in os.environ:
          seed = int(os.environ["PYTHONHASHSEED"]) + worker_id
      else:
          seed = np.random.get_state()[1][0] + worker_id  # type: ignore
      set_seed(seed)
  
imports: import logging
  import math
  import os
  from typing import Any, DefaultDict, Dict, List, Optional, Tuple, Union, no_type_check
  import networkx
  import numpy
  import pandas
  import pyarrow.parquet
  import torch
  from sklearn.model_selection import GroupShuffleSplit, train_test_split
  from torch import distributed
  from torch.utils.data import DataLoader, Sampler, SequentialSampler
  from llm_studio.src.datasets.conversation_chain_handler import ConversationChainHandler
  from llm_studio.src.utils.exceptions import LLMDataException
  from llm_studio.src.utils.utils import PatchedAttribute, set_seed
label_desc: Use image node_id 10 for calling a global function with example usage: worker_init_fn(worker_id) without return types