code: def gbm_monotone_smoke_test():
      cal_housing = fetch_california_housing()
      data = h2o.H2OFrame(
          cal_housing.data, column_names=cal_housing.feature_names
      )
  
      data["target"] = h2o.H2OFrame(cal_housing.target)
  
      train, test = data.split_frame([0.6], seed=123)
  
      feature_names = ["MedInc", "AveOccup", "HouseAge"]
      monotone_constraints = {
          "MedInc": 1,
          "AveOccup": -1,
          "HouseAge": 1,
      }
  
      gbm_mono = H2OGradientBoostingEstimator(
          monotone_constraints=monotone_constraints, seed=42
      )
      gbm_mono.train(
          x=feature_names,
          y="target",
          training_frame=train,
          validation_frame=test,
      )
  
imports: import h2o
  from h2o.estimators import H2OGradientBoostingEstimator
  from tests import pyunit_utils
  from sklearn.datasets import fetch_california_housing
label_desc: Use image node_id 1 for calling a global function with example usage: gbm_monotone_smoke_test() without return types