code: def clustering_fun(graph, lib):
      if lib == "nx":
          coefficients = netlib.clustering(graph.G)
      else:
          coefficients = {}
          for node in range(graph.num_nodes):
              coefficients[node] = graph.G._graph.GetNodeClustCf(node)
      feature = torch.tensor(
          [coefficients[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 3 for calling a global function with example usage: clustering_fun(graph, lib) without return types