code: def test_gradient_correct(self) -> None:
      # Tests that gradient is the same with DP or with DDP
      n_gpus = torch.cuda.device_count()
      self.assertTrue(
          n_gpus >= 2,
          f"Need at least 2 gpus but was provided only {n_gpus}.",
      )
  
      for clipping in ["flat", "per_layer"]:
          for grad_sample_mode in ["hooks", "ew"]:
              weight_dp, weight_nodp = torch.zeros(10, 10), torch.zeros(
                  10, 10
              )
  
              run_demo(
                  demo_basic,
                  weight_dp,
                  2,
                  dp=True,
                  clipping=clipping,
                  grad_sample_mode=grad_sample_mode,
              )
              run_demo(
                  demo_basic,
                  weight_nodp,
                  2,
                  dp=False,
                  clipping=None,
                  grad_sample_mode=None,
              )
  
              self.assertTrue(
                  torch.allclose(
                      weight_dp, weight_nodp, atol=1e-5, rtol=1e-3
                  )
              )
  
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 the GradientComputationTest obj's underlying member method code with example usage: obj.test_gradient_correct() without return types