code: def __init__(
      self,
      data_dir: str,
      batch_size: int,
      sizes: List[int],
      in_memory: bool = False,
  ):
      super().__init__()
      self.data_dir = data_dir
      self.batch_size = batch_size
      self.sizes = sizes
      self.in_memory = in_memory
  
imports: import argparse
  import glob
  import os
  import os.path
  import time
  from typing import List, NamedTuple, Optional
  import numpy
  import torch
  import torch.nn.functional
  from ogb.lsc import MAG240MDataset, MAG240MEvaluator
  import pytorch_lightning
  from pytorch_lightning import LightningDataModule, LightningModule, Trainer, seed_everything
  from pytorch_lightning.callbacks import ModelCheckpoint
  from torchmetrics import Accuracy
  from torch import Tensor
  from torch.nn import BatchNorm1d, Dropout, Linear, ModuleList, ReLU, Sequential
  from torch.optim.lr_scheduler import StepLR
  from torch_geometric.data import NeighborSampler
  from torch_geometric.nn import GATConv, SAGEConv
  from torch_sparse import SparseTensor
  from tqdm.auto import tqdm
  from root import ROOT
label_desc: Use image node_id 1 to create a new MAG240M object from inherited base classes: LightningDataModule with example: obj = MAG240M(data_dir, batch_size, sizes, in_memory)