code: def test_predict():
      rs = np.random.RandomState(1)
      X = rs.rand(20, 10)
      Y = rs.rand(10, 1)
      model = RandomForest(configspace=_get_cs(10))
      model.train(X[:10], Y[:10])
      m_hat, v_hat = model.predict(X[10:])
      assert m_hat.shape == (10, 1)
      assert v_hat.shape == (10, 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 3 for calling a global function with example usage: test_predict() without return types