code: def test_step(self, batch, batch_idx):
      data, target = batch
      output = self(data)
      loss = F.cross_entropy(output, target)
      self.test_accuracy(output, target)
      self.log(
          "test_loss", loss, on_step=False, on_epoch=True, prog_bar=True
      )
      self.log(
          "test_accuracy",
          self.test_accuracy,
          on_step=False,
          on_epoch=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 5 for calling the LitSampleConvNetClassifier obj's underlying member method code with example usage: obj.test_step(batch, batch_idx) and returns: loss