code: def setUp(self):
      self.original_model = SampleConvNet()
      copy_of_original_model = SampleConvNet()
      copy_of_original_model.load_state_dict(
          self.original_model.state_dict(), strict=True
      )
  
      self.grad_sample_module = self.CLS(
          copy_of_original_model,
          batch_first=True,
          loss_reduction="mean",
      )
      self.DATA_SIZE = 8
      self.setUp_data()
      self.criterion = nn.L1Loss()
  
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 1 for calling the GradSampleModuleTest obj's underlying member method code with example usage: obj.setUp() without return types