code: def __init__(self, batch_size: int, data_dir: str = PATH_DATASETS):
      super().__init__()
      self.data_dir = data_dir
      self.transform = transforms.Compose(
          [
              transforms.ToTensor(),
              transforms.Normalize((0.1307,), (0.3081,)),
          ]
      )
  
      self.batch_size = batch_size
      self.dims = (1, 28, 28)
      self.num_classes = 10
  
imports: import math
  import os
  import torch
  from filelock import FileLock
  import pytorch_lightning
  from torch.nn import functional
  from torch.utils.data import DataLoader, random_split
  from torchmetrics import Accuracy
  from torchvision import transforms
  from torchvision.datasets import MNIST
  from ray.tune.integration.pytorch_lightning import TuneReportCheckpointCallback
  from ray import train, tune
label_desc: Use image node_id 1 to create a new MNISTDataModule object from inherited base classes: pl with example: obj = MNISTDataModule(batch_size, data_dir)