code: def demo_ddp_hook(
      rank, world_size, weight, dp, noise_multiplier, max_grad_norm
  ):
      torch.manual_seed(rank)
      batch_size = 32
      withdp = "with" + ("out " if not dp else "")
      print(
          f"Running DDP hook {withdp} differential privacy example on rank {rank}."
      )
  
      device = setup_and_get_device(rank, world_size, nonce=1)
  
      # create model and move it to GPU with id rank
      model = ToyModel().to(device)
  
      model.net1.bias.requires_grad = False
      model.net2.bias.requires_grad = False
      model.net2.weight.requires_grad = False
  
      ddp_model = DDP(model, device_ids=[device])
  
      if dp:
          engine = PrivacyEngine(
              ddp_model,
              batch_size=batch_size,
              sample_size=10 * batch_size,
              alphas=PRIVACY_ALPHAS,
              noise_multiplier=noise_multiplier,
              max_grad_norm=[max_grad_norm],
          )
          engine.random_number_generator = engine._set_seed(0)
  
      loss_fn = nn.MSELoss()
      optimizer = optim.SGD(ddp_model.parameters(), lr=1)
      if dp:
          engine.attach(optimizer)
  
      optimizer.zero_grad()
      labels = torch.randn(batch_size, 5).to(device)
  
      outputs = ddp_model(torch.randn(batch_size, 10).to(device))
      loss_fn(outputs, labels).backward()
      optimizer.step()
  
      weight.copy_(model.net1.weight.data.cpu())
  
      del ddp_model
      cleanup()
  
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 4 for calling a global function with example usage: demo_ddp_hook(rank, world_size, weight, dp, noise_multiplier, max_grad_norm) without return types