code: def test_standard_module_validation(self):
      class SimpleLinear(nn.Module):
          def __init__(self, in_f, out_f):
              super().__init__()
              self.p = nn.Parameter(torch.Tensor(in_f, out_f))
  
          def forward(self, x: torch.Tensor):
              return F.linear(x, self.p)
  
      # Should be handled by functorch
      try:
          gsm = GradSampleModule(SimpleLinear(4, 2))
          self.assertTrue(
              hasattr(gsm._module, "ft_compute_sample_grad")
          )
      except ImportError:
          print("Test could not be ran because functorch not available")
  
      # Should not raise exception if strict=False
      try:
          GradSampleModule(SimpleLinear(4, 2), strict=False)
      except ImportError:
          print("Test could not be ran because functorch not available")
  
      # Should not fail after relevant grad sampler has been registered
      register_grad_sampler(SimpleLinear)(compute_linear_grad_sample)
      GradSampleModule(SimpleLinear(4, 2))
  
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 9 for calling the GradSampleModuleTest obj's underlying member method code with example usage: obj.test_standard_module_validation() and returns: F