code: def test_rollup_stats():
      df = h2o.import_file(
          pyunit_utils.locate("smalldata/glm_test/rollup_stat_test.csv")
      )
      df["RACE"] = df["RACE"].asfactor()  # this is important
      glm = H2OGeneralizedLinearEstimator(
          generate_scoring_history=True,
          score_iteration_interval=5,
          non_negative=True,
          alpha=[0.5, 1.0],
          standardize=False,
          nfolds=5,
          seed=7,
      )
      try:
          glm.train(y="RACE", training_frame=df)
      except (OSError, EnvironmentError) as e:
          # Keeping this test as it might be useful once beta constraints are supported
          # with multinomial family.
          assert (
              "non_negative:  does not work with multinomial family."
              in str(e)
          )
  
label_desc: Use image node_id 1 for calling a global function with example usage: test_rollup_stats() without return types