code: def test_ddp_hook(self) -> None:
      # Tests that the DDP hook does the same thing as naive aggregation with per layer clipping
      weight_ddp_naive, weight_ddp_hook = torch.zeros(
          10, 10
      ), torch.zeros(10, 10)
  
      run_function(
          demo_basic,
          weight_ddp_naive,
          dp=True,
          noise_multiplier=0.1,
          max_grad_norm=1.0,
      )
  
      run_function(
          demo_ddp_hook,
          weight_ddp_hook,
          dp=True,
          noise_multiplier=0.1,
          max_grad_norm=1.0,
      )
  
      self.assertTrue(
          torch.norm(weight_ddp_naive - weight_ddp_hook) < 1e-7,
          f"DDP naive: {weight_ddp_naive}\nDDP hook: {weight_ddp_hook}",
      )
  
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 3 for calling the GradientComputationTest obj's underlying member method code with example usage: obj.test_ddp_hook() without return types