code: def drf_mojo_reproducibility_info():
      problems = ["binomial", "multinomial", "regression"]
      PROBLEM = problems[randint(0, (len(problems) - 1))]
      TESTROWS = 2000
      df = pyunit_utils.random_dataset(
          PROBLEM, verbose=False, NTESTROWS=TESTROWS
      )
      train = df[TESTROWS:, :]
      x = list(set(df.names) - {"respose"})
      params = {"ntrees": 50, "max_depth": 4}
      drfModel = pyunit_utils.build_save_model_generic(
          params, x, train, "response", "drf", tempfile.mkdtemp()
      )
  
      isinstance(
          drfModel._model_json["output"][
              "reproducibility_information_table"
          ][1]["h2o_cluster_uptime"][0],
          float,
      )
      isinstance(
          drfModel._model_json["output"][
              "reproducibility_information_table"
          ][0]["java_version"][0],
          str,
      )
      assert (
          drfModel._model_json["output"][
              "reproducibility_information_table"
          ][2]["input_frame"][0]
          == "training_frame"
      )
  
imports: import sys
  from tests import pyunit_utils
  from random import randint
  import tempfile
label_desc: Use image node_id 1 for calling a global function with example usage: drf_mojo_reproducibility_info() without return types