code: def training_step(self, batch, batch_idx):
      data, target = batch
      output = self(data)
      loss = F.cross_entropy(output, target)
      self.log(
          "train_loss",
          loss,
          on_step=False,
          on_epoch=True,
          prog_bar=True,
      )
      return loss
  
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 4 for calling the LitSampleConvNetClassifier obj's underlying member method code with example usage: obj.training_step(batch, batch_idx) and returns: loss