code: def pyg_ego(args, pyg_dataset):
      avg_time = 0
      for i in range(args.num_runs):
          if args.print_run:
              print("Run {}".format(i + 1))
          graphs = []
          ds = pyg_dataset[:373]
          from torch_geometric.data import DataLoader
  
          loader = DataLoader(ds, batch_size=1)
          s = time.time()
          for batch in loader:
              x, edge_index = pyg_ego_nets(batch)
          avg_time += time.time() - s
      print(
          "Tensor has average time: {}".format(avg_time / args.num_runs)
      )
  
file_path: deepsnap/benchmark/transform/ego_net.py
imports: import copy
  import time
  import torch
  import argparse
  import numpy
  from torch.utils.data import DataLoader
  from deepsnap.batch import Batch
  from deepsnap.dataset import GraphDataset
  from torch_geometric.datasets import TUDataset
label_desc: Use image node_id 5 for calling a global function with example usage: pyg_ego(args, pyg_dataset) without return types