code: def test_predict_marginalized_over_instances_no_features():
      """The GP should fall back to the regular predict() method."""
  
      class Dummy:
          counter = 0
  
          def __call__(self, X, Y=None):
              self.counter += 1
  
      dummy = Dummy()
      with patch.object(MCMCGaussianProcess, "predict", dummy.__call__):
          seed = 1
          rs = np.random.RandomState(seed)
          X = rs.rand(20, 10)
          Y = rs.rand(10, 1)
          model = get_gp(10, seed)
          model.train(X[:10], Y[:10])
          model.predict(X[10:])
          assert dummy.counter == 1
  
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 6 for calling a global function with example usage: test_predict_marginalized_over_instances_no_features() without return types