code: def training_step(self, batch, batch_idx):
      x, y = batch
      logits = self.forward(x)
      loss = F.cross_entropy(logits, y)
      self.log("train_loss", loss, prog_bar=True)
      self.log("train_acc", self.train_acc(logits, y), prog_bar=True)
      return {"loss": loss}
  
imports: import operator
  import torch
  import torch.nn.functional
  from lightning.pytorch import LightningModule
  from lightning_utilities.core.imports import compare_version
  from torch import nn
  from torchmetrics import Accuracy, MeanSquaredError
label_desc: Use image node_id 4 for calling the ClassificationModel obj's underlying member method code with example usage: obj.training_step(batch, batch_idx) and returns: dict