code: def gbm_mojo_reproducibility_info():
      prostate_hex = h2o.import_file(
          pyunit_utils.locate("smalldata/testng/prostate.csv")
      )
  
      model = H2OIsolationForestEstimator()
      model.train(training_frame=prostate_hex)
  
      print("Downloading Java prediction model code from H2O")
      TMPDIR = os.path.normpath(
          os.path.join(
              os.path.dirname(os.path.realpath("__file__")),
              "..",
              "results",
              model._id,
          )
      )
      os.makedirs(TMPDIR)
      mojo_path = model.download_mojo(path=TMPDIR)
      gbmModel = h2o.upload_mojo(mojo_path=mojo_path)
  
      isinstance(
          gbmModel._model_json["output"][
              "reproducibility_information_table"
          ][1]["h2o_cluster_uptime"][0],
          float,
      )
      isinstance(
          gbmModel._model_json["output"][
              "reproducibility_information_table"
          ][0]["java_version"][0],
          str,
      )
      assert (
          gbmModel._model_json["output"][
              "reproducibility_information_table"
          ][2]["input_frame"][0]
          == "training_frame"
      )
  
imports: import sys, os
  import h2o
  from tests import pyunit_utils
  from h2o.estimators.isolation_forest import H2OIsolationForestEstimator
label_desc: Use image node_id 1 for calling a global function with example usage: gbm_mojo_reproducibility_info() without return types