code: def test_train_with_pca():
      rs = np.random.RandomState(1)
      X = rs.rand(20, 20)
      Y = rs.rand(20, 1)
  
      F = {}
      for i in range(10):
          F[f"instance-{i}"] = list(rs.rand(10))
  
      model = RandomForest(
          configspace=_get_cs(10),
          pca_components=2,
          instance_features=F,
      )
      model.train(X, Y)
  
      assert model._n_features == 10
      assert model._n_hps == 10
      assert model._pca is not None
      assert model._scaler is not None
  
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 4 for calling a global function with example usage: test_train_with_pca() without return types