code: def add_remove_ddp_hooks(
      rank,
      world_size,
      remaining_hooks,
      dp,
      noise_multiplier=0,
      max_grad_norm=1e8,
  ):
      device = setup_and_get_device(rank, world_size, nonce=2)
  
      model = ToyModel().to(device)
      ddp_model = nn.parallel.DistributedDataParallel(
          model, device_ids=[device]
      )
  
      engine = PrivacyEngine(
          ddp_model,
          batch_size=1,
          sample_size=10,
          alphas=PRIVACY_ALPHAS,
          noise_multiplier=noise_multiplier,
          max_grad_norm=[max_grad_norm],
      )
  
      optimizer = optim.SGD(ddp_model.parameters(), lr=1)
  
      engine.attach(optimizer)
  
      remaining_hooks["attached"] = {
          p: p._backward_hooks
          for p in engine.module.parameters()
          if p._backward_hooks
      }
      engine.detach()
  
      remaining_hooks["detached"] = {
          p: p._backward_hooks
          for p in engine.module.parameters()
          if p._backward_hooks
      }
  
      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 5 for calling a global function with example usage: add_remove_ddp_hooks(rank, world_size, remaining_hooks, dp, noise_multiplier, max_grad_norm) without return types