code: def page_fun(graph, lib):
      if lib == "nx":
          pages = netlib.pagerank(graph.G)
      elif lib == "sx":
          pages = graph.G._graph.GetPageRank()
      feature = torch.tensor(
          [pages[node] for node in range(graph.num_nodes)],
          dtype=torch.float32,
      ).view(graph.num_nodes, 1)
      graph.node_feature = torch.cat(
          [graph.node_feature, feature], dim=-1
      )
  
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 2 for calling a global function with example usage: page_fun(graph, lib) without return types