code: def configure_optimizers(self):
      optimizer = optim.SGD(self.parameters(), lr=self.lr, momentum=0)
  
      if self.enable_dp:
          data_loader = (
              # soon there will be a fancy way to access train dataloader,
              # see https://github.com/PyTorchLightning/pytorch-lightning/issues/10430
              self.trainer._data_connector._train_dataloader_source.dataloader()
          )
  
          # transform (model, optimizer, dataloader) to DP-versions
          if hasattr(self, "dp"):
              self.dp["model"].remove_hooks()
          (
              dp_model,
              optimizer,
              dataloader,
          ) = self.privacy_engine.make_private(
              module=self,
              optimizer=optimizer,
              data_loader=data_loader,
              noise_multiplier=self.noise_multiplier,
              max_grad_norm=self.max_grad_norm,
              poisson_sampling=isinstance(data_loader, DPDataLoader),
          )
          self.dp = {"model": dp_model}
  
      return optimizer
  
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 3 for calling the LitSampleConvNetClassifier obj's underlying member method code with example usage: obj.configure_optimizers() and returns: optimizer