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(
          f"| distributed init (rank {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 datetime
  import errno
  import os
  import time
  from collections import defaultdict, deque
  import torch
  import torch.distributed
label_desc: Use image node_id 11 for calling a global function with example usage: init_distributed_mode(args) without return types