code: def forward(self, x):
      # x of shape [B, 1, 28, 28]
      x = F.relu(self.conv1(x))  # -> [B, 16, 14, 14]
      x = F.max_pool2d(x, 2, 1)  # -> [B, 16, 13, 13]
      x = F.relu(self.conv2(x))  # -> [B, 32, 5, 5]
      x = F.max_pool2d(x, 2, 1)  # -> [B, 32, 4, 4]
      x = x.view(-1, 32 * 4 * 4)  # -> [B, 512]
      x = F.relu(self.fc1(x))  # -> [B, 32]
      x = self.fc2(x)  # -> [B, 10]
      return x
  
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 2 for calling the SampleConvNet obj's underlying member method code with example usage: obj.forward(x) and returns: x