code: def test_normalize(self):
      self.gp_model._train(self.X_all, self.Y_all, do_optimize=False)
      y_in_mean = np.mean(self.Y_in)
      y_in_std = np.std(self.Y_in)
      np.testing.assert_allclose(
          self.gp_model.gp_model.y_out.numpy(),
          (self.Y_out - y_in_mean) / y_in_std,
      )
  
      rs = np.random.RandomState(1)
      model_unnormalize, configspace = generate_lgpga(
          self.kernel, self.num_dims, rs, normalize_y=False
      )
      model_unnormalize._train(
          self.X_all, self.Y_all, do_optimize=False
      )
      np.testing.assert_allclose(
          model_unnormalize.gp_model.y_out.numpy(), self.Y_out
      )
  
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 5 for calling the TestLGPGA obj's underlying member method code with example usage: obj.test_normalize() without return types