code: def test_gp_train():
      seed = 1
      rs = np.random.RandomState(seed)
      X = rs.rand(20, 10)
      Y = rs.rand(10, 1)
  
      fixture = np.array(
          [
              0.693147,
              0.0,
              0.0,
              0.0,
              0.0,
              0.0,
              0.0,
              0.0,
              0.0,
              0.0,
              0.0,
              -6.907755,
          ]
      )
  
      model = get_gp(10, seed)
      np.testing.assert_array_almost_equal(model._kernel.theta, fixture)
      model.train(X[:10], Y[:10])
      assert len(model.models) == 36
  
      for base_model in model.models:
          theta = base_model._gp.kernel.theta
          theta_ = base_model._gp.kernel_.theta
  
          # Test that the kernels of the base GP are actually changed!
          np.testing.assert_array_almost_equal(theta, theta_)
          assert not np.any(theta == fixture)
          assert not np.any(theta_ == fixture)
  
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 3 for calling a global function with example usage: test_gp_train() without return types