code: def test_with_ordinal():
      cs = ConfigurationSpace(seed=0)
      _ = cs.add_hyperparameter(
          CategoricalHyperparameter("a", [0, 1], default_value=0)
      )
      _ = cs.add_hyperparameter(
          OrdinalHyperparameter("b", [0, 1], default_value=1)
      )
      _ = cs.add_hyperparameter(
          UniformFloatHyperparameter(
              "c", lower=0.0, upper=1.0, default_value=1
          )
      )
      _ = cs.add_hyperparameter(
          UniformIntegerHyperparameter(
              "d", lower=0, upper=10, default_value=1
          )
      )
  
      F = {}
      for i in range(1):
          F[f"instance-{i}"] = [0, 0, 0]
  
      model = RandomForest(
          configspace=cs,
          instance_features=F,
          ratio_features=1.0,
          pca_components=9,
      )
  
      X = np.array(
          [
              [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
              [0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0],
              [0.0, 1.0, 0.0, 9.0, 0.0, 0.0, 0.0],
              [0.0, 1.0, 1.0, 4.0, 0.0, 0.0, 0.0],
          ],
          dtype=np.float64,
      )
      y = np.array([0, 1, 2, 3], dtype=np.float64)
  
      X_train = np.vstack((X, X, X, X, X, X, X, X, X, X))
      y_train = np.vstack((y, y, y, y, y, y, y, y, y, y))
  
      model.train(X_train, y_train.reshape((-1, 1)))
      mean, _ = model.predict(X)
      for idx, m in enumerate(mean):
          assert pytest.approx(y[idx], 0.05) == m
  
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 10 for calling a global function with example usage: test_with_ordinal() without return types