code: def validation_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
      self.log("val_loss", self.loss(y_logits, y_true), prog_bar=True)
  
      # 3. Compute accuracy:
      self.log(
          "val_acc",
          self.valid_acc(y_scores, y_true.int()),
          prog_bar=True,
      )
  
imports: import logging
  from pathlib import Path
  from typing import Union
  import numpy
  import torch
  import torch.nn.functional
  from torch import nn, optim
  from torch.optim.lr_scheduler import MultiStepLR
  from torch.optim.optimizer import Optimizer
  from torch.utils.data import DataLoader, Subset
  from torchmetrics.classification import MulticlassAccuracy
  from torchvision import models, transforms
  from torchvision.datasets import ImageFolder
  from torchvision.datasets.utils import download_and_extract_archive
  from pytorch_lightning import LightningDataModule, LightningModule
  from pytorch_lightning.callbacks.finetuning import BaseFinetuning
  from pytorch_lightning.utilities.rank_zero import rank_zero_info
label_desc: Use image node_id 6 for calling the TransferLearningModel obj's underlying member method code with example usage: obj.validation_step(batch, batch_idx) without return types