code: def test_predict_marginalized():
      rs = np.random.RandomState(1)
      X = rs.rand(20, 10)
      F = {}
      for i in range(10):
          F[f"instance-{i}"] = list(rs.rand(5))
      Y = rs.rand(len(X) * len(F), 1)
      X_ = rs.rand(200, 15)
  
      model = RandomForest(
          configspace=_get_cs(10),
          instance_features=F,
      )
      model.train(X_, Y)
      means, variances = model.predict_marginalized(X)
      assert means.shape == (20, 1)
      assert variances.shape == (20, 1)
  
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 7 for calling a global function with example usage: test_predict_marginalized() without return types