code: def test_zero_grad(self):
      x, _ = next(iter(self.dl))
      self.original_model = self.original_model.train()
      self.grad_sample_module = self.grad_sample_module.train()
      gs_out = self.grad_sample_module(x)
      loss = self.criterion(gs_out, torch.zeros_like(gs_out))
      loss.backward()
  
      self.grad_sample_module.zero_grad()
      params_with_gs = [
          n
          for n, p in self.grad_sample_module.named_parameters()
          if p.grad_sample is not None
      ]
      msg = (
          "After calling .zero_grad() on the GradSampleModule, the following parameters still "
          f"have a grad_sample: {params_with_gs}"
      )
      assert len(params_with_gs) == 0, msg
  
imports: import unittest
  import torch
  import torch.nn
  import torch.nn.functional
  from opacus.grad_sample import GradSampleModule
  from opacus.grad_sample.gsm_exp_weights import GradSampleModuleExpandedWeights
  from opacus.grad_sample.linear import compute_linear_grad_sample
  from opacus.grad_sample.utils import register_grad_sampler
  from torch.testing import assert_close
  from torch.utils.data import DataLoader
  from torchvision import transforms
  from torchvision.datasets import FakeData
  from torchvision.models import mobilenet_v3_small
label_desc: Use image node_id 4 for calling the GradSampleModuleTest obj's underlying member method code with example usage: obj.test_zero_grad() without return types