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 os
  import warnings
  import pytorch_lightning
  import torch.nn
  import torch.nn.functional
  import torch.optim
  import torchmetrics
  from opacus import PrivacyEngine
  from opacus.data_loader import DPDataLoader
  from opacus.lightning import DPLightningDataModule
  from pl_bolts.datamodules import MNISTDataModule
  from pytorch_lightning.utilities.cli import LightningCLI
label_desc: Use image node_id 2 for calling the LitSampleConvNetClassifier obj's underlying member method code with example usage: obj.forward(x) and returns: x