code: def pyg_cluster(args, pyg_dataset):
      avg_time = 0
      for i in range(args.num_runs):
          if args.print_run:
              print("Run {}".format(i + 1))
          s = time.time()
          res = []
          for data in pyg_dataset:
              feature = tensor_clustering_coefficient(
                  data.edge_index, data.num_nodes
              )
              data.x = torch.cat([data.x, feature], dim=-1)
              res.append(data)
          avg_time += time.time() - s
      print(
          "Tensor 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 9 for calling a global function with example usage: pyg_cluster(args, pyg_dataset) without return types