code: def infer_init_method(
      cfg: DistributedTrainingConfig, force_distributed=False
  ):
      if cfg.distributed_init_method is not None or cfg.tpu:
          return
  
      num_pipelines_per_node = None
      if cfg.pipeline_model_parallel:
          (
              num_pipeline_devices,
              num_pipelines_per_node,
          ) = _pipeline_parallel_pre_init(cfg)
  
      if cfg.distributed_world_size == 1:
          return
      if all(
          key in os.environ
          for key in [
              "MASTER_ADDR",
              "MASTER_PORT",
              "WORLD_SIZE",
              "RANK",
          ]
      ):
          # support torch.distributed.launch
          _infer_torch_distributed_launch_init(cfg)
      else:
          # we can determine the init method automatically for Slurm
          if not _infer_slurm_init(cfg, num_pipelines_per_node):
              if cfg.distributed_port <= 0 or force_distributed:
                  _infer_single_node_init(cfg)
          elif cfg.distributed_port <= 0:
              _infer_single_node_init(cfg)
  
      if cfg.pipeline_model_parallel:
          _pipeline_parallel_post_init(
              cfg, num_pipeline_devices, num_pipelines_per_node
          )
      elif not cfg.distributed_no_spawn:
          with open_dict(cfg):
              cfg.distributed_num_procs = min(
                  torch.cuda.device_count(), cfg.distributed_world_size
              )
      else:
          if cfg.device_id > 0:
              logger.info(
                  "setting CUDA device={} on rank {}".format(
                      cfg.device_id, cfg.distributed_rank
                  )
              )
              torch.cuda.set_device(cfg.device_id)
  
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 2 for calling a global function with example usage: infer_init_method(cfg, force_distributed) without return types