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"))
  
      if self.is_hetero:
          (
              train_idx_dict,
              valid_idx_dict,
              test_idx_dict,
          ) = read_nodesplitidx_split_hetero(path)
          for nodetype in train_idx_dict.keys():
              train_idx_dict[nodetype] = train_idx_dict[nodetype]
              valid_idx_dict[nodetype] = valid_idx_dict[nodetype]
              test_idx_dict[nodetype] = test_idx_dict[nodetype]
  
              return {
                  "train": train_idx_dict,
                  "valid": valid_idx_dict,
                  "test": test_idx_dict,
              }
  
      else:
          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": train_idx,
              "valid": valid_idx,
              "test": test_idx,
          }
  
file_path: ogb/ogb/nodeproppred/dataset.py
imports: import pandas
  import shutil, os
  import os.path
  from ogb.utils.url import decide_download, download_url, extract_zip
  from ogb.io.read_graph_raw import read_csv_graph_raw, read_csv_heterograph_raw, read_node_label_hetero, read_nodesplitidx_split_hetero, read_binary_graph_raw, read_binary_heterograph_raw
  import torch
  import numpy
label_desc: Use image node_id 3 for calling the NodePropPredDataset obj's underlying member method code with example usage: obj.get_idx_split(split_type) and returns: torch, dict, dict