code: def test_predict_with_actual_values():
      X = np.array(
          [
              [0.0, 0.0, 0.0],
              [0.0, 0.0, 1.0],
              [0.0, 1.0, 0.0],
              [0.0, 1.0, 1.0],
              [1.0, 0.0, 0.0],
              [1.0, 0.0, 1.0],
              [1.0, 1.0, 0.0],
              [1.0, 1.0, 1.0],
          ],
          dtype=np.float64,
      )
      y = np.array(
          [
              [0.1],
              [0.2],
              [9],
              [9.2],
              [100.0],
              [100.2],
              [109.0],
              [109.2],
          ],
          dtype=np.float64,
      )
      model = RandomForest(
          configspace=_get_cs(3),
          instance_features=None,
          seed=12345,
          ratio_features=1.0,
      )
      model.train(
          np.vstack((X, X, X, X, X, X, X, X)),
          np.vstack((y, y, y, y, y, y, y, y)),
      )
  
      y_hat, _ = model.predict(X)
      for y_i, y_hat_i in zip(
          y.reshape((1, -1)).flatten(), y_hat.reshape((1, -1)).flatten()
      ):
          assert pytest.approx(y_i, 0.1) == y_hat_i
  
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 9 for calling a global function with example usage: test_predict_with_actual_values() without return types