code: def xgb_mojo_reproducibility_info():
      df = h2o.import_file(
          path=pyunit_utils.locate(
              "smalldata/gbm_test/ecology_model.csv"
          )
      )
      df["Angaus"] = df["Angaus"].asfactor()
      df["Weights"] = h2o.H2OFrame.from_python(
          abs(np.random.randn(df.nrow, 1)).tolist()
      )[0]
      print(df.col_names)
      train, calib = df.split_frame(
          ratios=[0.8],
          destination_frames=["eco_train", "eco_calib"],
          seed=42,
      )
  
      model = H2OXGBoostEstimator(
          ntrees=100,
          distribution="bernoulli",
          min_rows=10,
          max_depth=5,
          weights_column="Weights",
          calibrate_model=True,
          calibration_frame=calib,
      )
      model.train(
          x=list(range(2, train.ncol)), y="Angaus", training_frame=train
      )
  
      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)
      xgbModel = h2o.upload_mojo(mojo_path=mojo_path)
  
      isinstance(
          xgbModel._model_json["output"][
              "reproducibility_information_table"
          ][1]["h2o_cluster_uptime"][0],
          float,
      )
      isinstance(
          xgbModel._model_json["output"][
              "reproducibility_information_table"
          ][0]["java_version"][0],
          str,
      )
      assert (
          xgbModel._model_json["output"][
              "reproducibility_information_table"
          ][2]["input_frame"][0]
          == "training_frame"
      )
      assert (
          xgbModel._model_json["output"][
              "reproducibility_information_table"
          ][2]["input_frame"][2]
          == "calibration_frame"
      )
  
imports: import sys, os
  import h2o
  from tests import pyunit_utils
  import numpy
  from h2o.estimators.xgboost import H2OXGBoostEstimator
label_desc: Use image node_id 1 for calling a global function with example usage: xgb_mojo_reproducibility_info() without return types