code: def kmeans_cluster(args):
      dataset = pgl.dataset.CoraDataset()
      graph_embedding = load_graph_embedding(args.embed_file)
      y = dataset.y
      num_classes = dataset.num_classes
  
      print("%s number of classes" % num_classes)
      print("%s number nodes" % len(y))
      centroids, _ = kmeans(graph_embedding, num_classes)
      result, _ = vq(graph_embedding, centroids)
      acc1 = metrics.accuracy_score(y, result)
  
      node_feat = dataset.graph.node_feat["words"]
      node_feat = whiten(node_feat)
      centroids, _ = kmeans(node_feat, num_classes)
      result, _ = vq(node_feat, centroids)
      acc2 = metrics.accuracy_score(y, result)
  
      print("The accuracy of graph embedding is %.4f" % (acc1))
      print("The accuracy of node features is %.4f" % (acc2))
  
imports: import argparse
  import numpy
  from scipy.cluster.vq import vq, kmeans, whiten
  from sklearn import metrics
  import pgl
label_desc: Use image node_id 2 for calling a global function with example usage: kmeans_cluster(args) without return types