code: def training_step(self, batch, batch_idx):
      x, y = batch
      y_hat = self.backbone(x)
      y_hat = self.classifier(y_hat)
      loss = F.cross_entropy(y_hat, y)
      self._loss.append(loss.item())
      return {"loss": loss}
  
file_path: pytorch-lightning/tests/parity_pytorch/models.py
imports: import torch
  import torch.nn.functional
  from lightning.pytorch.core.module import LightningModule
  from lightning.pytorch.utilities.imports import _TORCHVISION_AVAILABLE
  from lightning.pytorch.utilities.model_helpers import get_torchvision_model
  from tests_pytorch import _PATH_DATASETS
  from torch.utils.data import DataLoader
label_desc: Use image node_id 2 for calling the ParityModuleCIFAR obj's underlying member method code with example usage: obj.training_step(batch, batch_idx) and returns: dict