code: def dl_mojo_reproducibility_info():
      # Training data
      train_data = h2o.import_file(
          path=tests.locate("smalldata/gbm_test/ecology_model.csv")
      )
      train_data = train_data.drop("Site")
      train_data["Angaus"] = train_data["Angaus"].asfactor()
      print(train_data.describe())
      train_data.head()
  
      # Testing data
      test_data = h2o.import_file(
          path=tests.locate("smalldata/gbm_test/ecology_eval.csv")
      )
      test_data["Angaus"] = test_data["Angaus"].asfactor()
      print(test_data.describe())
      test_data.head()
  
      # Run DeepLearning
      model = H2ODeepLearningEstimator(
          loss="CrossEntropy", epochs=1000, hidden=[20, 20, 20]
      )
      model.train(
          x=list(range(1, train_data.ncol)),
          y="Angaus",
          training_frame=train_data,
          validation_frame=test_data,
      )
  
      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)
      dlModel = h2o.upload_mojo(mojo_path=mojo_path)
  
      isinstance(
          dlModel._model_json["output"][
              "reproducibility_information_table"
          ][1]["h2o_cluster_uptime"][0],
          float,
      )
      isinstance(
          dlModel._model_json["output"][
              "reproducibility_information_table"
          ][0]["java_version"][0],
          str,
      )
      assert (
          dlModel._model_json["output"][
              "reproducibility_information_table"
          ][2]["input_frame"][0]
          == "training_frame"
      )
      assert (
          dlModel._model_json["output"][
              "reproducibility_information_table"
          ][2]["input_frame"][1]
          == "validation_frame"
      )
  
imports: import sys, os
  from h2o.two_dim_table import H2OTwoDimTable
  import h2o, tests
  from tests import pyunit_utils
  from h2o.estimators.deeplearning import H2ODeepLearningEstimator
label_desc: Use image node_id 1 for calling a global function with example usage: dl_mojo_reproducibility_info() without return types