code: def test_predict_with_actual_values(self):
      self.gp_model._train(self.X_all, self.Y_all, do_optimize=False)
      self.assertFalse(hasattr(self.gp_model.gp_model, "covar_module"))
  
      self.gp_model._train(self.X_all, self.Y_all, do_optimize=True)
      self.assertTrue(hasattr(self.gp_model.gp_model, "covar_module"))
  
      X = np.array(
          [
              [0.0, 0.0, 0.0],
              [0.0, 0.0, 1.0],
              [0.0, 1.0, 0.0],
              [0.0, 1.0, 1.0],
              [1.0, 0.0, 0.0],
              [1.0, 0.0, 1.0],
              [1.0, 1.0, 0.0],
              [1.0, 1.0, 1.0],
              [-1.0, -1.0, -1.0],
              [-1.0, -1.0, 2.0],
              [-1.0, 2.0, -1.0],
              [-1.0, 2.0, 2.0],
              [2.0, -1.0, -1.0],
              [2.0, -1.0, 2.0],
              [2.0, 2.0, -1.0],
              [2.0, 2.0, 2.0],
          ],
          dtype=np.float64,
      )
      y = np.array(
          [
              [0.1],
              [0.2],
              [9],
              [9.2],
              [100.0],
              [100.2],
              [109.0],
              [109.2],
              [1.0],
              [1.2],
              [14.0],
              [14.2],
              [110.0],
              [111.2],
              [129.0],
              [129.2],
          ],
          dtype=np.float64,
      )
      rs = np.random.RandomState(1)
      num_inducing = 4
      model, _ = generate_lgpga(
          kernel=generate_kernel(3),
          n_dimensions=3,
          rs=rs,
          num_inducing=num_inducing,
      )
      model.train(np.vstack((X, X, X)), np.vstack((y, y, y)))
  
      self.assertEqual(model.is_trained, True)
  
      self.assertTrue(hasattr(self.gp_model.gp_model, "covar_module"))
      mu_hat, var_hat = model.predict(np.array([[0.5, 0.5, 0.5]]))
  
      self.assertAlmostEqual(mu_hat[0][0], 54.612500000000004)
      # There's a slight difference between my local installation and travis
      self.assertLess(abs(var_hat[0][0] - 1026.149240121437), 15)
  
imports: from typing import Tuple
  import unittest.mock
  import gpytorch
  import numpy
  import pyro
  import torch
  from gpytorch.constraints.constraints import Interval
  from gpytorch.likelihoods.gaussian_likelihood import GaussianLikelihood
  from gpytorch.priors import HorseshoePrior
  from smac.configspace import ConfigurationSpace, UniformFloatHyperparameter
  from smac.model.gaussian_process.augmented_local_gaussian_process import AugmentedLocalGaussianProcess, GloballyAugmentedLocalGaussianProcess
  from smac.model.gaussian_process.gpytorch_gaussian_process import ExactGaussianProcessModel
  from ._test_boing_kernel import generate_kernel, generate_test_data
  from ._test_gp_gpytorch import TestGPGPyTorch
label_desc: Use image node_id 8 for calling the TestLGPGA obj's underlying member method code with example usage: obj.test_predict_with_actual_values() without return types