code: def setup(rank, world_size):
      if sys.platform == "win32":
          raise ValueError(
              "Windows platform is not supported for this test"
          )
      else:
          os.environ["MASTER_ADDR"] = "localhost"
          os.environ["MASTER_PORT"] = "12355"
  
          # initialize the process group
  
          os.environ["RANK"] = str(rank)
          os.environ["WORLD_SIZE"] = str(world_size)
          torch.distributed.init_process_group(
              init_method="env://",
              backend="nccl",
          )
  
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 opacus.optimizers.ddp_perlayeroptimizer import DistributedPerLayerOptimizer, SimpleDistributedPerLayerOptimizer
  from opacus.optimizers.ddpoptimizer import DistributedDPOptimizer
  from torch.nn.parallel import DistributedDataParallel
  from torch.utils.data import DataLoader, TensorDataset
  from torch.utils.data.distributed import DistributedSampler
label_desc: Use image node_id 1 for calling a global function with example usage: setup(rank, world_size) without return types