code: def test_to_standard_module(self):
      copy_of_original_model = SampleConvNet()
      copy_of_original_model.load_state_dict(
          self.original_model.state_dict(),
          strict=True,
      )
      new_grad_sample_module = GradSampleModule(
          copy_of_original_model,
          batch_first=True,
          loss_reduction="mean",
      )
  
      new_grad_sample_module = (
          new_grad_sample_module.to_standard_module()
      )
  
      assert isinstance(
          new_grad_sample_module, type(self.original_model)
      )
  
      original_state_dict = self.original_model.state_dict()
      gs_state_dict = new_grad_sample_module.state_dict()
  
      missing_keys = gs_state_dict.keys() - original_state_dict.keys()
      assert (
          not missing_keys
      ), f"The following keys are missing: {missing_keys}"
  
      extra_keys = original_state_dict.keys() - gs_state_dict.keys()
      assert (
          not extra_keys
      ), f"The following keys are extra: {extra_keys}"
  
      for key in original_state_dict:
          original_tensor = original_state_dict[key].float()
          gs_tensor = gs_state_dict[key].float()
          msg = (
              f"Param {key}: GradSample L2 norm = : {gs_tensor.norm(2)}, ",
              f"Original L2 norm = : {original_tensor.norm(2)}, ",
              f"MSE = {F.mse_loss(gs_tensor, original_tensor)}, ",
              f"L1 Loss = {F.l1_loss(gs_tensor, original_tensor)}",
          )
  
          assert_close(
              gs_tensor, original_tensor, atol=1e-6, rtol=1e-4, msg=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 5 for calling the GradSampleModuleTest obj's underlying member method code with example usage: obj.test_to_standard_module() without return types