code: def init_distributed_mode(args):
      if "RANK" in os.environ and "WORLD_SIZE" in os.environ:
          args.rank = int(os.environ["RANK"])
          args.world_size = int(os.environ["WORLD_SIZE"])
          args.gpu = int(os.environ["LOCAL_RANK"])
      elif "SLURM_PROCID" in os.environ:
          args.rank = int(os.environ["SLURM_PROCID"])
          args.gpu = args.rank % torch.cuda.device_count()
      else:
          print("Not using distributed mode")
          args.distributed = False
          return
  
      args.distributed = True
  
      torch.cuda.set_device(args.gpu)
      args.dist_backend = "nccl"
      print(
          "| distributed init (rank {}): {}".format(
              args.rank, args.dist_url
          ),
          flush=True,
      )
      torch.distributed.init_process_group(
          backend=args.dist_backend,
          init_method=args.dist_url,
          world_size=args.world_size,
          rank=args.rank,
      )
      torch.distributed.barrier()
      setup_for_distributed(args.rank == 0)
  
imports: import os
  import subprocess
  import time
  from collections import defaultdict, deque
  import datetime
  import pickle
  from packaging import version
  from typing import Optional, List
  import torch
  import torch.distributed
  from torch import Tensor
  import torchvision
label_desc: Use image node_id 14 for calling a global function with example usage: init_distributed_mode(args) without return types