code: import numpy as np
  from gcn.graphconv import ap_approximate
  
  
  def Model17(adj, alpha, y_train, y_test):
      k = int(np.ceil(4 * alpha))
      prediction, time = ap_approximate(adj, y_train, alpha, k)
      predicted_labels = np.argmax(prediction, axis=1)
      prediction = np.zeros(prediction.shape)
      prediction[np.arange(prediction.shape[0]), predicted_labels] = 1
  
      test_acc = np.sum(prediction * y_test) / np.sum(y_test)
      test_acc_of_class = np.sum(prediction * y_test, axis=0) / np.sum(y_test, axis=0)
      return test_acc, test_acc_of_class
  
apis: numpy.sum numpy.ceil numpy.zeros numpy.argmax numpy.arange