code: def comparison_test():
      if sys.version_info.major == 2:
          print("native XGBoost tests only supported on python3")
          return
      if sys.version_info.major == 3 and sys.version_info.minor >= 9:
          print(
              "native XGBoost tests only doesn't run on Python 3.{0} for now.".format(
                  sys.version_info.minor
              )
          )
          return
      import xgboost as xgb
  
      assert H2OXGBoostEstimator.available() is True
      ret = h2o.cluster()
      if len(ret.nodes) == 1:
          data = h2o.import_file(
              pyunit_utils.locate(
                  "smalldata/jira/adult_data_modified.csv"
              )
          )
          data[14] = data[14].asfactor()
          myX = list(range(0, 13))  # use column indices
          print(myX)
          y = "income"
          h2oParamsD = {
              "ntrees": 30,
              "max_depth": 4,
              "seed": 2,
              "learn_rate": 0.7,
              "col_sample_rate_per_tree": 0.9,
              "min_rows": 5,
              "score_tree_interval": 30 + 1,
              "tree_method": "exact",
              "backend": "cpu",
          }
  
          h2oModelD = H2OXGBoostEstimator(**h2oParamsD)
          # gather, print and save performance numbers for h2o model
          h2oModelD.train(x=myX, y=y, training_frame=data)
          h2oPredictD = h2oModelD.predict(data)
  
          nativeXGBoostParam = (
              h2oModelD.convert_H2OXGBoostParams_2_XGBoostParams()
          )
          nativeXGBoostInput = data.convert_H2OFrame_2_DMatrix(
              myX, y, h2oModelD
          )
  
          nativeModel = xgb.train(
              params=nativeXGBoostParam[0],
              dtrain=nativeXGBoostInput,
              num_boost_round=nativeXGBoostParam[1],
          )
          nativePred = nativeModel.predict(
              data=nativeXGBoostInput, ntree_limit=nativeXGBoostParam[1]
          )
          pyunit_utils.summarizeResult_binomial(
              h2oPredictD, nativePred, -1, -1, -1, -1, tolerance=1e-10
          )
      else:
          print(
              "********  Test skipped.  This test cannot be performed in multinode environment."
          )
  
label_desc: Use image node_id 1 for calling a global function with example usage: comparison_test() without return types