code: def gbm_monotone_quantile_test():
      train = h2o.import_file(
          path=pyunit_utils.locate("smalldata/iris/iris.csv")
      )
      train.describe()
  
      # Run GBM
      my_gbm = H2OGradientBoostingEstimator(
          distribution="quantile",
          monotone_constraints={"C2": 1},
          nfolds=5,
          quantile_alpha=0.8,
      )
      my_gbm.train(
          x=["C2", "C3", "C4", "C5"], y="C1", training_frame=train
      )
      my_gbm.show()
  
      my_gbm_metrics = my_gbm.model_performance(train)
      my_gbm_metrics.show()
  
      print(my_gbm_metrics)
  
label_desc: Use image node_id 1 for calling a global function with example usage: gbm_monotone_quantile_test() without return types