code: def get_native_parameters_test():
      assert H2OXGBoostEstimator.available() is True
      ntrees = 17
      # CPU Backend is forced for the results to be comparable
      h2oParamsS = {
          "ntrees": ntrees,
          "max_depth": 4,
          "seed": 1,
          "learn_rate": 0.7,
          "col_sample_rate_per_tree": 0.9,
          "min_rows": 5,
          "score_tree_interval": ntrees + 1,
          "dmatrix_type": "sparse",
          "tree_method": "exact",
          "backend": "cpu",
      }
  
      nrows = 1000
      ncols = 10
      factorL = 11
      numCols = 0
      enumCols = ncols - numCols
  
      trainFile = pyunit_utils.genTrainFrame(
          nrows,
          0,
          enumCols=enumCols,
          enumFactors=factorL,
          miscfrac=0.1,
          randseed=17,
      )
      print(trainFile)
      myX = trainFile.names
      y = "response"
      myX.remove(y)
  
      h2oModelS = H2OXGBoostEstimator(**h2oParamsS)
      h2oModelS.train(x=myX, y=y, training_frame=trainFile)
  
      print(
          h2oModelS._model_json["output"][
              "native_parameters"
          ].as_data_frame()
      )
  
      assert (
          h2oModelS._model_json["output"][
              "native_parameters"
          ]._table_header
          == "Native XGBoost Parameters"
      )
  
label_desc: Use image node_id 1 for calling a global function with example usage: get_native_parameters_test() without return types