code: def test_automl_creates_interpretable_SE_with_only_monotonic_models():
      ds = import_dataset()
      aml_mono = H2OAutoML(
          project_name="test_automl_creates_interpretable_se",
          max_models=5,
          include_algos=["GBM", "GLM", "XGBoost", "StackedEnsemble"],
          monotone_constraints=dict(
              AGE=1, DPROS=1, DCAPS=1, PSA=1, VOL=1, GLEASON=1
          ),
          seed=1234,
      )
      aml_mono.train(y=ds.target, training_frame=ds.train)
  
      leaderboard = aml_mono.leaderboard.as_data_frame()["model_id"]
  
      assert leaderboard.apply(
          lambda model_name: "Monotonic" in model_name
      ).any()
  
      se_name = leaderboard[
          leaderboard.apply(
              lambda model_name: "Monotonic" in model_name
          )
      ]
      se_mono = h2o.get_model(se_name.iloc[0])
  
      assert leaderboard.apply(
          lambda model_name: "GLM" in model_name
      ).any()
      assert all(
          ["GBM" in bm or "XGBoost" in bm for bm in se_mono.base_models]
      )
  
imports: import sys, os
  import h2o
  from h2o.automl import H2OAutoML
  from tests import pyunit_utils
  from _automl_utils import import_dataset
label_desc: Use image node_id 2 for calling a global function with example usage: test_automl_creates_interpretable_SE_with_only_monotonic_models() without return types