code: def normalize_transform(self):
      return transforms.Normalize(
          mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]
      )
  
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 4 for calling the CatDogImageDataModule obj's underlying member method code with example usage: obj.normalize_transform() and returns: transforms