code: def setup_distributed(rank, world_size):
      os.environ["MASTER_ADDR"] = "localhost"
      os.environ["MASTER_PORT"] = "12355"
  
      # initialize the process group
      torch.distributed.init_process_group(
          "nccl", rank=rank, world_size=world_size
      )
  
imports: import argparse
  import logging
  import os
  import string
  from datetime import datetime
  from time import time
  import torch
  import torchaudio
  from ctc_decoders import GreedyDecoder
  from datasets import collate_factory, split_process_librispeech
  from languagemodels import LanguageModel
  from torch.optim import Adadelta, Adam, AdamW, SGD
  from torch.optim.lr_scheduler import ExponentialLR, ReduceLROnPlateau
  from torch.utils.data import DataLoader
  from torchaudio.functional import edit_distance
  from torchaudio.models.wav2letter import Wav2Letter
  from transforms import Normalize, UnsqueezeFirst
  from utils import count_parameters, MetricLogger, save_checkpoint
label_desc: Use image node_id 2 for calling a global function with example usage: setup_distributed(rank, world_size) without return types