code: def interaction_constraint_test():
      assert H2OXGBoostEstimator.available() is True
  
      # CPU Backend is forced for the results to be comparable
      h2o_params = {
          "tree_method": "exact",
          "seed": 123,
          "backend": "cpu",
          "ntrees": 5,
      }
  
      train = pyunit_utils.genTrainFrame(
          100, 10, enumCols=0, randseed=17
      )
      print(train)
      x = train.names
      y = "response"
      x.remove(y)
  
      h2o_params["interaction_constraints"] = [
          ["C1", "C2"],
          ["C3", "C4", "C5"],
      ]
  
      model = H2OXGBoostEstimator(**h2o_params)
      model.train(x=x, y=y, training_frame=train)
  
      native_params = model._model_json["output"][
          "native_parameters"
      ].as_data_frame()
      print(native_params)
  
      constraints = (
          native_params[
              native_params["name"] == "interaction_constraints"
          ]
      )["value"].values[0]
  
      assert constraints == "[[0,1],[2,3,4]]", (
          "Constraints should be [[0,1],[2,3,4]] but it is:"
          + constraints
      )
  
label_desc: Use image node_id 1 for calling a global function with example usage: interaction_constraint_test() without return types