code: def test_predict_with_actual_values():
      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],
          ],
          dtype=np.float64,
      )
      y = np.array(
          [
              [0.1],
              [0.2],
              [9],
              [9.2],
              [100.0],
              [100.2],
              [109.0],
              [109.2],
          ],
          dtype=np.float64,
      )
      seed = 1
      rs = np.random.RandomState(seed)
      model = get_gp(3, seed)
      model.train(
          np.vstack((X, X, X, X, X, X, X, X)),
          np.vstack((y, y, y, y, y, y, y, y)),
      )
  
      mu_hat, var_hat = model.predict(X)
      for y_i, y_hat_i, mu_hat_i in zip(
          y.reshape((1, -1)).flatten(),
          mu_hat.reshape((1, -1)).flatten(),
          var_hat.reshape((1, -1)).flatten(),
      ):
          assert pytest.approx(y_hat_i, 0.0001) == y_i
          assert round(mu_hat_i) == 0
  
      # Regression test that performance does not drastically decrease in the near future
      mu_hat, var_hat = model.predict(np.array([[10, 10, 10]]))
      assert pytest.approx(mu_hat[0][0]) == 54.612500000000004
      # There's a slight difference between my local installation and travis
      # assert abs(var_hat[0][0] - 1121.8409184001594) < 2
  
      # test other covariance results
      _, var_fc = model.predict(X, covariance_type="full")
      assert var_fc.shape == (8, 8)
  
      _, var_sd = model.predict(X, covariance_type="diagonal")
      assert var_sd.shape == (8, 1)
  
      _, var_no = model.predict(
          np.array([[10, 10, 10]]), covariance_type=None
      )
      assert var_no is None
  
      # check values
      _, var_fc = model.predict(
          np.array([[10, 10, 10]]), covariance_type="full"
      )
      assert pytest.approx(var_fc[0][0]) == var_hat[0][0]
  
      _, var_sd = model.predict(
          np.array([[10, 10, 10]]), covariance_type="std"
      )
      assert pytest.approx(var_sd[0][0] ** 2) == var_hat[0][0]
  
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 11 for calling a global function with example usage: test_predict_with_actual_values() without return types