code: def test_get_gp(self):
      self.assertIsNone(self.gp_model.gp)
      self.gp_model._get_gp(self.X_in, self.Y_in)
      self.assertIsInstance(
          self.gp_model.gp_model, ExactGaussianProcessModel
      )
  
      self.gp_model._get_gp(
          self.X_in, self.Y_in, self.X_out, self.Y_out
      )
      self.assertIsInstance(
          self.gp_model.gp_model, AugmentedLocalGaussianProcess
      )
  
      # num_outer is not enough, we return to a vanilla GP model
      self.gp_model._train(self.X_in, self.Y_in, do_optimize=False)
      self.assertIsInstance(
          self.gp_model.gp_model, ExactGaussianProcessModel
      )
  
      self.gp_model._train(self.X_all, self.Y_all, do_optimize=False)
      self.assertIsInstance(
          self.gp_model.gp_model, AugmentedLocalGaussianProcess
      )
      self.assertFalse(self.gp_model.gp_model.augmented)
      self.assertFalse(hasattr(self.gp_model.gp_model, "covar_module"))
  
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 4 for calling the TestLGPGA obj's underlying member method code with example usage: obj.test_get_gp() without return types