code: def setup_ddp(args):
      # Set the local_rank, rank, and world_size values as args fields
      # This is done differently depending on how we're running the script. We
      # currently support either torchrun or the custom run_with_submitit.py
      # If you're confused (like I was), this might help a bit
      # https://discuss.pytorch.org/t/what-is-the-difference-between-rank-and-local-rank/61940/2
  
      if all(
          key in os.environ
          for key in ("LOCAL_RANK", "RANK", "WORLD_SIZE")
      ):
          # if we're here, the script was called with torchrun. Otherwise,
          # these args will be set already by the run_with_submitit script
          args.local_rank = int(os.environ["LOCAL_RANK"])
          args.rank = int(os.environ["RANK"])
          args.world_size = int(os.environ["WORLD_SIZE"])
  
      elif "gpu" in args:
          # if we're here, the script was called by run_with_submitit.py
          args.local_rank = args.gpu
      else:
          print("Not using distributed mode!")
          args.distributed = False
          args.world_size = 1
          return
  
      args.distributed = True
  
      _redefine_print(is_main=(args.rank == 0))
  
      torch.cuda.set_device(args.local_rank)
      dist.init_process_group(
          backend="nccl",
          rank=args.rank,
          world_size=args.world_size,
          init_method=args.dist_url,
      )
      torch.distributed.barrier()
  
imports: import datetime
  import os
  import time
  from collections import defaultdict, deque
  import torch
  import torch.distributed
  import torch.nn.functional
label_desc: Use image node_id 4 for calling a global function with example usage: setup_ddp(args) without return types