code: def demo_xgboost_concurrent_contributions():
      prostate_path = pyunit_utils.locate(
          "smalldata/logreg/prostate.csv"
      )
  
      prostate = h2o.import_file(path=prostate_path)
      prostate["CAPSULE"] = prostate["CAPSULE"].asfactor()
  
      xgb_model = H2OXGBoostEstimator()
      xgb_model.train(
          x=["AGE", "RACE", "DPROS", "DCAPS", "PSA", "VOL", "GLEASON"],
          y="CAPSULE",
          training_frame=prostate,
      )
  
      results_dir = os.path.join(
          pyunit_utils.locate("results"), "xgb_concurrent"
      )
      os.mkdir(results_dir)
      mojo_path = xgb_model.download_mojo(
          results_dir, get_genmodel_jar=True
      )
  
      # how many parallel threads to run
      concurrency = 4
  
      reference_result = h2o.mojo_predict_csv(
          input_csv_path=prostate_path,
          mojo_zip_path=mojo_path,
          output_csv_path=os.path.join(results_dir, "predictions.csv"),
          predict_contributions=True,
          extra_cmd_args=["--testConcurrent", str(concurrency)],
      )
      print(reference_result)
  
      for test_id in range(4):
          with open(
              os.path.join(
                  results_dir, "predictions.csv." + str(test_id)
              )
          ) as csv_file:
              concurrent_result = list(csv.DictReader(csv_file))
              assert reference_result == concurrent_result
  
imports: import sys
  import os
  import csv
  import h2o
  from tests import pyunit_utils
  from h2o.estimators.xgboost import H2OXGBoostEstimator
label_desc: Use image node_id 1 for calling a global function with example usage: demo_xgboost_concurrent_contributions() without return types