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)
      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)
      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
      )
  
      func = gp.sample_functions(X_test=X_test, n_funcs=2)
      func_prime = gp_norm.sample_functions(X_test=X_test, n_funcs=2)
      np.testing.assert_array_almost_equal(func, func_prime, decimal=1)
  
imports: from unittest.mock import patch
  import numpy
  import pytest
  import scipy.optimize
  import sklearn.datasets
  import sklearn.model_selection
  from ConfigSpace import CategoricalHyperparameter, ConfigurationSpace, EqualsCondition, UniformFloatHyperparameter
  from smac.model.gaussian_process.gaussian_process import GaussianProcess
  from smac.model.gaussian_process.priors import HorseshoePrior, LogNormalPrior
  from smac.utils.configspace import convert_configurations_to_array
label_desc: Use image node_id 14 for calling a global function with example usage: test_normalization() without return types