code: def glm_mojo_reproducibility_info():
      params = {
          "family": "fractionalbinomial",
          "alpha": [0],
          "lambda_": [0],
          "standardize": False,
          "compute_p_values": True,
      }
      train = h2o.import_file(
          pyunit_utils.locate(
              "smalldata/glm_test/fraction_binommialOrig.csv"
          )
      )
      x = ["log10conc"]
      y = "y"
  
      glmModel = pyunit_utils.build_save_model_generic(
          params, x, train, y, "glm", tempfile.mkdtemp()
      )  # build and save mojo model
  
      isinstance(
          glmModel._model_json["output"][
              "reproducibility_information_table"
          ][1]["h2o_cluster_uptime"][0],
          float,
      )
      isinstance(
          glmModel._model_json["output"][
              "reproducibility_information_table"
          ][0]["java_version"][0],
          str,
      )
      assert (
          glmModel._model_json["output"][
              "reproducibility_information_table"
          ][2]["input_frame"][0]
          == "training_frame"
      )
  
imports: import sys
  import h2o
  from tests import pyunit_utils
  import tempfile
label_desc: Use image node_id 1 for calling a global function with example usage: glm_mojo_reproducibility_info() without return types