code: def training_step(self, batch, batch_idx):
      # 1. Forward pass:
      x, y = batch
      y_logits = self.forward(x)
      y_scores = torch.sigmoid(y_logits)
      y_true = y.view((-1, 1)).type_as(x)
  
      # 2. Compute loss
      train_loss = self.loss(y_logits, y_true)
  
      # 3. Compute accuracy:
      self.log(
          "train_acc",
          self.train_acc(y_scores, y_true.int()),
          prog_bar=True,
      )
  
      return train_loss
  
imports: import logging
  from pathlib import Path
  from typing import Union
  import torch
  import torch.nn.functional
  from lightning.pytorch import LightningDataModule, LightningModule, cli_lightning_logo
  from lightning.pytorch.callbacks.finetuning import BaseFinetuning
  from lightning.pytorch.cli import LightningCLI
  from lightning.pytorch.utilities import rank_zero_info
  from lightning.pytorch.utilities.model_helpers import get_torchvision_model
  from torch import nn, optim
  from torch.optim.lr_scheduler import MultiStepLR
  from torch.optim.optimizer import Optimizer
  from torch.utils.data import DataLoader
  from torchmetrics import Accuracy
  from torchvision import transforms
  from torchvision.datasets import ImageFolder
  from torchvision.datasets.utils import download_and_extract_archive
label_desc: Use image node_id 5 for calling the TransferLearningModel obj's underlying member method code with example usage: obj.training_step(batch, batch_idx) and returns: train_loss