code: def __init__(self, dataset, fanouts, batch_size, num_workers):
      super().__init__()
      self.fanouts = fanouts
      self.batch_size = batch_size
      self.num_workers = num_workers
      self.feature_store = dataset.feature
      self.graph = dataset.graph
      self.train_set = dataset.tasks[0].train_set
      self.valid_set = dataset.tasks[0].validation_set
      self.num_classes = dataset.tasks[0].metadata["num_classes"]
  
imports: import argparse
  import dgl.graphbolt
  import dgl.nn.pytorch
  import torch
  import torch.nn
  import torch.nn.functional
  from pytorch_lightning import LightningDataModule, LightningModule, Trainer
  from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
  from torchmetrics import Accuracy
label_desc: Use image node_id 1 to create a new DataModule object from inherited base classes: LightningDataModule with example: obj = DataModule(dataset, fanouts, batch_size, num_workers)