code: def test_MultiObjectiveDummyEnsemble(backend):
      negative_root_mean_squared_error = make_scorer(
          "negative_root_mean_squared_error",
          sklearn.metrics.mean_squared_error,
          optimum=0,
          worst_possible_result=-MAXINT,
          greater_is_better=True,
          squared=False,
      )
  
      ensemble = MultiObjectiveDummyEnsemble(
          task_type=REGRESSION,
          metrics=[
              root_mean_squared_error,
              negative_root_mean_squared_error,
          ],
          random_state=0,
          backend=backend,
      )
  
      # fit the whole thing
      X_data = np.random.random(size=(100, 2))
      y_true = np.full((100), 5.5)
      predictions = []
      for i in range(20, 1, -1):
          pred = np.ones((100), dtype=np.float32) * i
          predictions.append(pred)
      identifiers = [(i, i, 0.0) for i in range(20)]
  
      ensemble.fit(
          base_models_predictions=predictions,
          X_data=X_data,
          true_targets=y_true,
          model_identifiers=identifiers,
          runs=[],
      )
  
      # Because the target value is 5.5 some of the predictions have the same error
      # -> not every prediction is on the Pareto front
      assert len(ensemble.pareto_set_) == 15
      for sub_ensemble in ensemble.pareto_set_:
          assert isinstance(sub_ensemble, SingleModelEnsemble)
  
      # test the prediction
      ensemble_prediction = np.random.random((1, 100, 2))
      prediction = ensemble.predict(ensemble_prediction)
      assert prediction.shape == (100, 2)
  
      assert (
          str(ensemble) == """MultiObjectiveDummyEnsemble: 15 models"""
      )
  
      # From here on everything is supposed to return the output of the best
      # ensemble according to the 1st metric
      models_with_weights = ensemble.get_models_with_weights(
          {identifier: i for i, identifier in enumerate(identifiers)}
      )
      assert models_with_weights == [(1.0, 14)]
  
      identifiers_with_weights = ensemble.get_identifiers_with_weights()
      assert identifiers_with_weights == [((14, 14, 0.0), 1.0)]
  
      selected_model_identifiers = (
          ensemble.get_selected_model_identifiers()
      )
      assert selected_model_identifiers == [(14, 14, 0.0)]
  
      best_model_score = ensemble.get_validation_performance()
      assert best_model_score == 0.5
  
imports: import numpy
  import sklearn.metrics
  from autosklearn.constants import REGRESSION
  from autosklearn.ensembles.multiobjective_dummy_ensemble import MultiObjectiveDummyEnsemble
  from autosklearn.ensembles.singlebest_ensemble import SingleModelEnsemble
  from autosklearn.metrics import MAXINT, make_scorer, root_mean_squared_error
label_desc: Use image node_id 1 for calling a global function with example usage: test_MultiObjectiveDummyEnsemble(backend) without return types