code: def load_dataset():
      """
      load raw datasets.
      :return: a list of networkx/deepsnap graphs, plus additional info if needed
      """
      format = cfg.dataset.format
      name = cfg.dataset.name
      # dataset_dir = '{}/{}'.format(cfg.dataset.dir, name)
      dataset_dir = cfg.dataset.dir
      # Try to load customized data format
      for func in register.loader_dict.values():
          graphs = func(format, name, dataset_dir)
          if graphs is not None:
              return graphs
      # Load from Pytorch Geometric dataset
      if format == "PyG":
          graphs = load_pyg(name, dataset_dir)
      # Load from networkx formatted data
      # todo: clean nx dataloader
      elif format == "nx":
          graphs = load_nx(name, dataset_dir)
      # Load from OGB formatted data
      elif cfg.dataset.format == "OGB":
          if cfg.dataset.name == "ogbg-molhiv":
              dataset = PygGraphPropPredDataset(name=cfg.dataset.name)
              graphs = GraphDataset.pyg_to_graphs(dataset)
          # Note this is only used for custom splits from OGB
          split_idx = dataset.get_idx_split()
          return graphs, split_idx
      else:
          raise ValueError(
              "Unknown data format: {}".format(cfg.dataset.format)
          )
      return graphs
  
imports: import logging
  import pickle
  import time
  import networkx
  import torch
  import torch_geometric.transforms
  from deepsnap.batch import Batch
  from deepsnap.dataset import GraphDataset
  from ogb.graphproppred import PygGraphPropPredDataset
  from torch.utils.data import DataLoader
  from torch_geometric.datasets import PPI, Amazon, Coauthor, KarateClub, MNISTSuperpixels, Planetoid, QM7b, TUDataset
  import graphgym.models.feature_augment
  import graphgym.register
  from graphgym.config import cfg
  from graphgym.models.transform import edge_nets, ego_nets, path_len, remove_node_feature
label_desc: Use image node_id 3 for calling a global function with example usage: load_dataset() and returns: graphs, graphs, graphs,  split_idx