code: def get_idx_split(self, split_type=None):
      if split_type is None:
          split_type = self.meta_info["split"]
  
      path = osp.join(self.root, "split", split_type)
  
      # short-cut if split_dict.pt exists
      if os.path.isfile(os.path.join(path, "split_dict.pt")):
          return torch.load(os.path.join(path, "split_dict.pt"))
  
      train_idx = pd.read_csv(
          osp.join(path, "train.csv.gz"),
          compression="gzip",
          header=None,
      ).values.T[0]
      valid_idx = pd.read_csv(
          osp.join(path, "valid.csv.gz"),
          compression="gzip",
          header=None,
      ).values.T[0]
      test_idx = pd.read_csv(
          osp.join(path, "test.csv.gz"), compression="gzip", header=None
      ).values.T[0]
  
      return {
          "train": torch.tensor(train_idx, dtype=torch.long),
          "valid": torch.tensor(valid_idx, dtype=torch.long),
          "test": torch.tensor(test_idx, dtype=torch.long),
      }
  
file_path: ogb/ogb/graphproppred/dataset_pyg.py
imports: import pandas
  import shutil, os
  import os.path
  import torch
  import numpy
  from torch_geometric.data import InMemoryDataset
  from ogb.utils.url import decide_download, download_url, extract_zip
  from ogb.io.read_graph_pyg import read_graph_pyg
label_desc: Use image node_id 2 for calling the PygGraphPropPredDataset obj's underlying member method code with example usage: obj.get_idx_split(split_type) and returns: dict, torch