code: def get_dataset(name, transform=RowFeatNormalizer(subtract_min=True)):
      dgl_dataset_dict = {
          "coauthor_cs": CoauthorCSDataset,
          "coauthor_physics": CoauthorPhysicsDataset,
          "amazon_computers": AmazonCoBuyComputerDataset,
          "amazon_photos": AmazonCoBuyPhotoDataset,
          "wiki_cs": get_wiki_cs,
          "ppi": get_ppi,
      }
  
      dataset_class = dgl_dataset_dict[name]
      train_data, val_data, test_data = None, None, None
      if name != "ppi":
          dataset = dataset_class(transform=transform)
      else:
          dataset, train_data, val_data, test_data = dataset_class()
  
      return dataset, train_data, val_data, test_data
  
file_path: dgl/examples/pytorch/bgrl/utils.py
imports: import copy
  import numpy
  import torch
  from dgl.data import AmazonCoBuyComputerDataset, AmazonCoBuyPhotoDataset, CoauthorCSDataset, CoauthorPhysicsDataset, PPIDataset, WikiCSDataset
  from dgl.dataloading import GraphDataLoader
  from dgl.transforms import Compose, DropEdge, FeatMask, RowFeatNormalizer
label_desc: Use image node_id 4 for calling a global function with example usage: get_dataset(name, transform) and returns: dataset,  train_data,  val_data,  test_data