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(GaussianProcess, "predict", dummy.__call__):
          seed = 1
          rs = np.random.RandomState(seed)
  
          # cont
          X, Y, n_dims = get_cont_data(rs)
          # cat
          X, Y, cat_dims, cont_dims = get_cat_data(rs)
  
          for ct, model in enumerate(
              (
                  get_gp(n_dims, seed),
                  get_mixed_gp(cat_dims, cont_dims, seed),
              )
          ):
              model.train(X[:10], Y[:10])
              model.predict(X[10:])
              assert dummy.counter == ct + 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 10 for calling a global function with example usage: test_predict_marginalized_over_instances_no_features() without return types