code: def distributed_init(cfg: FairseqConfig):
      if isinstance(cfg, Namespace):
          from fairseq.dataclass.utils import (
              convert_namespace_to_omegaconf,
          )
  
          cfg = convert_namespace_to_omegaconf(cfg)
  
      if not cfg.common.tpu:
          if (
              torch.distributed.is_available()
              and torch.distributed.is_initialized()
          ):
              warnings.warn(
                  "Distributed is already initialized, cannot initialize twice!"
              )
          else:
              logger.info(
                  "distributed init (rank {}): {}".format(
                      cfg.distributed_training.distributed_rank,
                      cfg.distributed_training.distributed_init_method,
                  )
              )
              dist.init_process_group(
                  backend=cfg.distributed_training.distributed_backend,
                  init_method=cfg.distributed_training.distributed_init_method,
                  world_size=cfg.distributed_training.distributed_world_size,
                  rank=cfg.distributed_training.distributed_rank,
              )
              logger.info(
                  "initialized host {} as rank {}".format(
                      socket.gethostname(),
                      cfg.distributed_training.distributed_rank,
                  )
              )
  
              # perform a dummy all-reduce to initialize the NCCL communicator
              if torch.cuda.is_available():
                  dist.all_reduce(torch.zeros(1).cuda())
  
          cfg.distributed_training.distributed_rank = (
              torch.distributed.get_rank()
          )
      else:
          assert (
              xm.xrt_world_size()
              == cfg.distributed_training.distributed_world_size
          )
          global _USE_XLA
          _USE_XLA = True
          cfg.distributed_training.device_id = xm.get_local_ordinal()
          cfg.distributed_training.distributed_rank = xm.get_ordinal()
          xm.rendezvous("distributed_init")  # wait for all workers
  
      if is_master(cfg.distributed_training):
          logging.getLogger().setLevel(logging.INFO)
      else:
          logging.getLogger().setLevel(logging.WARNING)
  
      if cfg.common.model_parallel_size > 1:
          try:
              from fairseq.model_parallel.megatron.mpu import (
                  initialize_model_parallel,
                  model_parallel_cuda_manual_seed,
              )
          except ImportError:
              raise ImportError(
                  "\n\nPlease install the megatron submodule:"
                  "\n\n  git submodule update --init "
                  "fairseq/model_parallel/megatron"
              )
          global _USE_MEGATRON
          _USE_MEGATRON = True
          initialize_model_parallel(cfg.common.model_parallel_size)
          model_parallel_cuda_manual_seed(cfg.common.seed)
          model_part_number = get_model_parallel_rank()
          cfg.checkpoint.checkpoint_suffix += "-model_part-{0}".format(
              model_part_number
          )
  
      if (
          hasattr(cfg, "model")
          and getattr(cfg.model, "base_layers", 0) > 0
      ):
          cfg.checkpoint.checkpoint_suffix = (
              f"-rank-{cfg.distributed_training.distributed_rank}"
          )
  
      return cfg.distributed_training.distributed_rank
  
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 8 for calling a global function with example usage: distributed_init(cfg) and returns: cfg