code: def mojo_predict_pandas_test(sandbox_dir):
      data = h2o.import_file(
          path=pyunit_utils.locate("smalldata/coxph_test/heart.csv")
      )
  
      input_csv = "%s/in.csv" % sandbox_dir
      output_csv = "%s/prediction.csv" % sandbox_dir
      h2o.export_file(data, input_csv)
  
      data["transplant"] = data["transplant"].asfactor()
      model = H2OCoxProportionalHazardsEstimator(
          stratify_by=["transplant"],
          start_column="start",
          stop_column="stop",
      )
      model.train(
          x=["age", "surgery", "transplant"],
          y="event",
          training_frame=data,
      )
  
      h2o_prediction = model.predict(data)
  
      # download mojo
      model_zip_path = os.path.join(sandbox_dir, "model.zip")
      genmodel_path = os.path.join(sandbox_dir, "h2o-genmodel.jar")
      download_mojo(model, model_zip_path)
      assert os.path.isfile(model_zip_path)
      assert os.path.isfile(genmodel_path)
  
      pandas_frame = pandas.read_csv(input_csv)
      mojo_prediction = h2o.mojo_predict_pandas(
          dataframe=pandas_frame,
          mojo_zip_path=model_zip_path,
          genmodel_jar_path=genmodel_path,
      )
  
      assert len(mojo_prediction) == h2o_prediction.nrow
      assert_frame_equal(
          h2o_prediction.as_data_frame(use_pandas=True),
          mojo_prediction,
          check_dtype=False,
      )
  
file_path: h2o-3/h2o-py/tests/testdir_algos/coxph/pyunit_coxph_mojo_predict.py
imports: import sys
  import tempfile
  import shutil
  import time
  import os
  import pandas
  from pandas.testing import assert_frame_equal
  import h2o
  from tests import pyunit_utils
  from h2o.estimators.coxph import H2OCoxProportionalHazardsEstimator
label_desc: Use image node_id 3 for calling a global function with example usage: mojo_predict_pandas_test(sandbox_dir) without return types