code: def monotone_constraint_test():
      assert H2OXGBoostEstimator.available() is True
  
      # CPU Backend is forced for the results to be comparable
      h2oParamsS = {
          "tree_method": "exact",
          "seed": 123,
          "backend": "cpu",
          "ntrees": 5,
      }
  
      trainFile = pyunit_utils.genTrainFrame(
          100, 10, enumCols=0, randseed=17
      )
      print(trainFile)
      myX = trainFile.names
      y = "response"
      myX.remove(y)
  
      h2oParamsS["monotone_constraints"] = {"C1": -1, "C3": 1, "C7": 1}
  
      h2oModelS = H2OXGBoostEstimator(**h2oParamsS)
      h2oModelS.train(x=myX, y=y, training_frame=trainFile)
  
      native_params = h2oModelS._model_json["output"][
          "native_parameters"
      ].as_data_frame()
      print(native_params)
  
      constraints = (
          native_params[native_params["name"] == "monotone_constraints"]
      )["value"].values[0]
  
      assert constraints == "(-1,0,1,0,0,0,1,0,0,0)"
  
label_desc: Use image node_id 1 for calling a global function with example usage: monotone_constraint_test() without return types