code: def worker_init_fn(worker_id: int) -> None:
      """
      Callback function for PyTorch DataLoader `worker_init_fn`.
      It can set different random seed for the transforms in different workers.
  
      """
      worker_info = torch.utils.data.get_worker_info()
      set_rnd(worker_info.dataset, seed=worker_info.seed)
  
imports: from __future__ import annotations
  import hashlib
  import json
  import logging
  import math
  import os
  import pickle
  import sys
  from collections import abc, defaultdict
  from collections.abc import Generator, Iterable, Mapping, Sequence, Sized
  from copy import deepcopy
  from functools import reduce
  from itertools import product, starmap, zip_longest
  from pathlib import PurePath
  from typing import Any
  import numpy
  import torch
  from torch.utils.data._utils.collate import default_collate
  from monai import config
  from monai.config.type_definitions import NdarrayOrTensor, NdarrayTensor, PathLike
  from monai.data.meta_obj import MetaObj
  from monai.utils import MAX_SEED, BlendMode, Method, NumpyPadMode, TraceKeys, convert_data_type, convert_to_dst_type, ensure_tuple, ensure_tuple_rep, ensure_tuple_size, fall_back_tuple, first, get_equivalent_dtype, issequenceiterable, look_up_option, optional_import, pytorch_after
label_desc: Use image node_id 16 for calling a global function with example usage: worker_init_fn(worker_id) without return types