code: def __init__(self):
      super().__init__()
      self.conv1 = nn.Conv2d(3, 16, 8, 2, padding=3)
      self.conv2 = nn.Conv2d(16, 32, 4, 2)
      self.fc1 = nn.Linear(32 * 4 * 4, 32)
      self.fc2 = nn.Linear(32, 10)
  
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 to create a new SampleConvNet object from inherited base classes: nn with example: obj = SampleConvNet()