code: def test_gbm_mangus():
      train = pyunit_utils.random_dataset(
          "regression"
      )  # generate random dataset
      test = train.drop("response")
      xname = list(set(train.names) - {"response"})
  
      model_original = H2OGradientBoostingEstimator(
          ntrees=10,
          max_depth=2,
          col_sample_rate=0.8,
          sample_rate=0.7,
          stopping_rounds=3,
          seed=1234,
          score_tree_interval=10,
          learn_rate=0.1,
          stopping_metric="rmse",
      )
  
      model_original.train(x=xname, y="response", training_frame=train)
      score_original_h2o = model_original.model_performance(test)
      print("H2O score on original test frame:")
      try:
          print(score_original_h2o)
      except:
          assert (
              False
          ), "Should not have failed here with empty model metrics message."
  
label_desc: Use image node_id 1 for calling a global function with example usage: test_gbm_mangus() without return types