code: def prepare_dataset(name):
      # maximum size of cycle graph
      k = 8
  
      path = os.path.join("./", "dataset", name)
      data_folder = os.path.join(path, "processed")
      os.makedirs(data_folder, exist_ok=True)
  
      data_file = os.path.join(
          data_folder, "cycle_graph_induced_{}.bin".format(k)
      )
  
      # try to load
      if os.path.exists(data_file):  # load
          print("Loading dataset from {}".format(data_file))
          g_list, split_idx = load_graphs(data_file)
      else:  # generate
          g_list, split_idx = generate_dataset(path, name)
          print("Saving dataset to {}".format(data_file))
          save_graphs(data_file, g_list, split_idx)
  
      return g_list, split_idx
  
file_path: dgl/examples/pytorch/ogb/directional_GSN/preprocessing.py
imports: import os
  import graph_tool
  import graph_tool.topology
  import networkx
  import numpy
  import torch
  from dgl.data.utils import load_graphs, save_graphs
  from ogb.graphproppred import DglGraphPropPredDataset
  from tqdm import tqdm
label_desc: Use image node_id 4 for calling a global function with example usage: prepare_dataset(name) and returns: g_list,  split_idx