class_docstr: This class tests the GPy Gaussian Process classifier.
code: def test_predict(self):
      # predictions should be correct
      self.assertTrue(
          np.mean(
              (
                  self.classifier.predict(self.x_test_iris[:3])[:, 0]
                  > 0.5
              )
              == self.y_test_iris_binary[:3]
          )
          > 0.6
      )
      outlier = np.ones(np.shape(self.x_test_iris[:3])) * 10.0
      # output for random points should be 0.5 (as classifier is uncertain)
      self.assertTrue(
          np.sum(self.classifier.predict(outlier).flatten() == 0.5)
          == 6.0
      )
  
file_path: adversarial-robustness-toolbox/tests/estimators/classification/test_GPy.py
imports: from __future__ import absolute_import, division, print_function, unicode_literals
  import logging
  import unittest
  import numpy
  import GPy
  from art.estimators.classification.GPy import GPyGaussianProcessClassifier
  from tests.utils import TestBase, master_seed
label_desc: Use image node_id 3 for calling the TestGPyGaussianProcessClassifier obj's underlying member method code with example usage: obj.test_predict() without return types