code: def setup_and_get_device(rank, world_size, nonce=0):
      """
      Initialize the torch.distributed process group.
      If you run multiple groups in parallel or if you have zombie processes, you can add a nonce to avoid errors.
      """
      device = 0
      if sys.platform == "win32":
          # Distributed package only covers collective communications with Gloo
          # backend and FileStore on Windows platform. Set init_method parameter
          # in init_process_group to a local file.
          # Example init_method="file:///f:/libtmp/some_file"
          init_method = "file:///{your local file path}"
  
          # initialize the process group
          dist.init_process_group(
              "gloo",
              init_method=init_method,
              rank=rank,
              world_size=world_size,
          )
          device = rank
      elif os.environ.get("SLURM_NTASKS") is not None:
          # Running on a Slurm cluster
          os.environ["MASTER_ADDR"] = "127.0.0.1"
          os.environ["MASTER_PORT"] = str(7440 + nonce)
          local_rank = int(os.environ.get("SLURM_LOCALID"))
          dist.init_process_group(
              backend="gloo", rank=rank, world_size=world_size
          )
  
          # The device is the local rank (if you have 2 nodes with 8 GPUs each, you will have two "cuda:0" devices)
          device = local_rank
      else:
          os.environ["MASTER_ADDR"] = "localhost"
          os.environ["MASTER_PORT"] = "12355"
  
          os.environ["RANK"] = str(rank)
          os.environ["WORLD_SIZE"] = str(world_size)
          dist.init_process_group(
              init_method="env://",
              backend="nccl",
          )
  
          # Single node experiment
          device = rank
      return device
  
imports: import os
  import sys
  import unittest
  import torch
  import torch.distributed
  import torch.multiprocessing
  import torch.nn
  import torch.optim
  from opacus import PrivacyEngine
  from opacus.distributed import DifferentiallyPrivateDistributedDataParallel
  from torch.nn.parallel import DistributedDataParallel
label_desc: Use image node_id 1 for calling a global function with example usage: setup_and_get_device(rank, world_size, nonce) and returns: device