code: def deepsnap_pagerank(args, pyg_dataset):
      avg_time = 0
      task = "graph"
      for i in range(args.num_runs):
          if args.print_run:
              print("Run {}".format(i + 1))
          graphs = GraphDataset.pyg_to_graphs(
              pyg_dataset,
              verbose=True,
              fixed_split=False,
              netlib=netlib,
          )
          dataset = GraphDataset(graphs, task=task)
          s = time.time()
          dataset.apply_transform(
              page_fun, update_tensor=False, lib=args.netlib
          )
          avg_time += time.time() - s
      print(
          "DeepSNAP has average time: {}".format(
              avg_time / args.num_runs
          )
      )
  
file_path: deepsnap/benchmark/transform/pagerank_clustering.py
imports: import time
  import torch
  import argparse
  import deepsnap
  import numpy
  from deepsnap.dataset import GraphDataset
  from torch_geometric.datasets import TUDataset
label_desc: Use image node_id 6 for calling a global function with example usage: deepsnap_pagerank(args, pyg_dataset) without return types