code: def _infer_torch_distributed_launch_init(
      cfg: DistributedTrainingConfig,
  ):
      cfg.distributed_init_method = "env://"
      cfg.distributed_world_size = int(os.environ["WORLD_SIZE"])
      cfg.distributed_rank = int(os.environ["RANK"])
      cfg.device_id = cfg.distributed_rank % torch.cuda.device_count()
      # processes are created by torch.distributed.launch
      cfg.distributed_no_spawn = True
  
imports: import io
  import logging
  import os
  import pickle
  import random
  import socket
  import struct
  import subprocess
  import warnings
  from argparse import Namespace
  from collections import OrderedDict
  from dataclasses import dataclass
  from typing import Any, Dict, List, Mapping, Optional
  import torch
  import torch.distributed
  from fairseq.dataclass.configs import DistributedTrainingConfig, FairseqConfig
  from omegaconf import open_dict
label_desc: Use image node_id 3 for calling a global function with example usage: _infer_torch_distributed_launch_init(cfg) without return types