code: def test_predict_marginalized_mocked():
      rs = np.random.RandomState(1)
      F = {}
      for i in range(10):
          F[f"instance-{i}"] = list(rs.rand(5))
  
      model = RandomForest(
          configspace=_get_cs(10),
          instance_features=F,
      )
      X = rs.rand(20, 10)
      Y = rs.randint(1, size=(len(X) * len(F), 1)) * 1.0
      X_ = rs.rand(200, 15)
      model.train(X_, Y)
      means, variances = model.predict_marginalized(rs.rand(11, 10))
      assert means.shape == (11, 1)
      assert variances.shape == (11, 1)
      for i in range(11):
          assert means[i] == 0.0
          assert variances[i] == 1.0e-10
  
file_path: SMAC3/tests/test_model/test_rf.py
imports: import numpy
  import pytest
  from ConfigSpace import CategoricalHyperparameter, ConfigurationSpace, EqualsCondition, OrdinalHyperparameter, UniformFloatHyperparameter, UniformIntegerHyperparameter
  from smac.model.random_forest.random_forest import RandomForest
  from smac.utils.configspace import convert_configurations_to_array
label_desc: Use image node_id 8 for calling a global function with example usage: test_predict_marginalized_mocked() without return types