code: def test_normalization():
      X = np.arange(-5, 5, 0.1).reshape((-1, 1))
      X_test = np.arange(-5.05, 5.05, 0.1).reshape((-1, 1))
      y = np.sin(X)
      seed = 1
      gp = get_gp(
          n_dimensions=1, seed=seed, noise=1e-10, normalize_y=False
      )
      gp._train(X, y, optimize_hyperparameters=False)
      assert not gp.models[0]._normalize_y
      assert not hasattr(gp.models[0], "mean_y_")
  
      mu_hat, var_hat = gp.predict(X_test)
      gp_norm = get_gp(
          n_dimensions=1, seed=seed, noise=1e-10, normalize_y=True
      )
      gp_norm._train(X, y, optimize_hyperparameters=False)
      assert gp_norm.models[0]._normalize_y
      assert hasattr(gp_norm.models[0], "mean_y_")
  
      mu_hat_prime, var_hat_prime = gp_norm.predict(X_test)
      np.testing.assert_array_almost_equal(
          mu_hat, mu_hat_prime, decimal=4
      )
      np.testing.assert_array_almost_equal(
          var_hat, var_hat_prime, decimal=4
      )
  
file_path: SMAC3/tests/test_model/test_gp_mcmc.py
imports: from unittest.mock import patch
  import numpy
  import pytest
  import sklearn.datasets
  import sklearn.model_selection
  from ConfigSpace import ConfigurationSpace, UniformFloatHyperparameter
  from smac.model.gaussian_process.mcmc_gaussian_process import MCMCGaussianProcess
  from smac.model.gaussian_process.priors import HorseshoePrior, LogNormalPrior
label_desc: Use image node_id 7 for calling a global function with example usage: test_normalization() without return types