code: def call_main(cfg: FairseqConfig, main, **kwargs):
      if cfg.distributed_training.distributed_init_method is None:
          infer_init_method(cfg.distributed_training)
  
      if cfg.distributed_training.distributed_init_method is not None:
          # distributed training
          if not cfg.distributed_training.distributed_no_spawn:
              start_rank = cfg.distributed_training.distributed_rank
              cfg.distributed_training.distributed_rank = (
                  None  # assign automatically
              )
              kwargs["start_rank"] = start_rank
  
              torch.multiprocessing.spawn(
                  fn=distributed_main,
                  args=(main, cfg, kwargs),
                  nprocs=min(
                      torch.cuda.device_count(),
                      cfg.distributed_training.distributed_world_size,
                  ),
                  join=True,
              )
          else:
              distributed_main(
                  cfg.distributed_training.device_id, main, cfg, kwargs
              )
      elif (
          cfg.common.tpu
          and cfg.distributed_training.distributed_world_size > 1
      ):
          import torch_xla.distributed.xla_multiprocessing as xmp
  
          torch.multiprocessing.set_sharing_strategy("file_system")
          xmp.spawn(
              fn=distributed_main,
              args=(main, cfg, kwargs),
              # tpu-comment:
              #   8 devices in one TPU VM, is the max processes to be spawned.
              #   The rest is driven by xm.distributed.xla_dist
              nprocs=min(
                  cfg.distributed_training.distributed_world_size, 8
              ),
          )
      else:
          # single GPU main
          main(cfg, **kwargs)
  
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 10 for calling a global function with example usage: call_main(cfg, main) without return types