code: def forward(self, x):
      # x of shape [B, 1, 28, 28]
      x = F.relu(self.conv1(x))  # -> [B, 16, 10, 10]
      x = F.max_pool2d(x, 2, 2)  # -> [B, 16, 5, 5]
      x = x.view(
          x.shape[0], x.shape[1], x.shape[2] * x.shape[3]
      )  # -> [B, 16, 25]
      x = F.relu(self.conv2(x))  # -> [B, 32, 23]
      x = self.convf(x)  # -> [B, 32, 23]
      x = self.convpf(x)  # -> [B, 32, 23]
      x = self.fc1(x)  # -> [B, 32, 17]
      x = x.view(-1, x.shape[-2] * x.shape[-1])  # -> [B, 32 * 17]
      x = self.fc2(x)  # -> [B, 10]
      return x
  
imports: import unittest
  from typing import Iterable, Optional
  import torch
  import torch.nn
  import torch.nn.functional
  from opacus import PrivacyEngine
  from opacus.grad_sample.utils import get_gsm_class
  from opacus.utils.batch_memory_manager import BatchMemoryManager
  from torch.utils.data import DataLoader
  from torchvision import transforms
  from torchvision.datasets import FakeData
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