class_docstr: This class tests the GPy Gaussian Process classifier.
code: def setUpClass(cls):
      master_seed(seed=1234)
      super().setUpClass()
  
      # change iris to binary problem, so it is learnable for GPC
      cls.y_train_iris_binary = cls.y_train_iris[:, 1]
      cls.y_test_iris_binary = cls.y_test_iris[:, 1]
  
      # set up GPclassifier
      gpkern = GPy.kern.RBF(np.shape(cls.x_train_iris)[1])
      m = GPy.models.GPClassification(
          cls.x_train_iris,
          cls.y_train_iris_binary.reshape(-1, 1),
          kernel=gpkern,
      )
      m.inference_method = (
          GPy.inference.latent_function_inference.laplace.Laplace()
      )
      m.optimize(messages=True, optimizer="lbfgs")
  
      # get ART classifier + clean accuracy
      cls.classifier = GPyGaussianProcessClassifier(m)
  
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 1 for calling the TestGPyGaussianProcessClassifier obj's underlying member method code with example usage: obj.setUpClass(cls) without return types