code: def test_coxph_enum_num_interactions_orders(
      interaction_orders, fileName
  ):
      # bunch of random columns to be added to the dataset
      data = h2o.import_file(pyunit_utils.locate(fileName))
      data["surgery"] = data["surgery"].asfactor()
      data["transplant"] = data["transplant"].asfactor()
  
      model = H2OCoxProportionalHazardsEstimator(
          start_column="start",
          stop_column="stop",
          interaction_pairs=interaction_orders,
      )
      model.train(
          x=["age", "surgery", "transplant", "C1", "C2", "C3", "C4"],
          y="event",
          training_frame=data,
      )
      pred_h2o = model.predict(data)
      # export new file (including the random columns)
      sandbox_dir = tempfile.mkdtemp()
      model.download_mojo(path=sandbox_dir)
      mojo_name = pyunit_utils.getMojoName(model._id)
      input_csv = "%s/in.csv" % sandbox_dir
      h2o.export_file(data, input_csv)
      x, pred_mojo = pyunit_utils.mojo_predict(
          model, sandbox_dir, mojo_name
      )
      pyunit_utils.compare_frames_local(
          pred_h2o, pred_mojo, 1, tol=1e-10
      )
  
imports: import sys
  import tempfile
  import h2o
  from tests import pyunit_utils
  from h2o.estimators.coxph import H2OCoxProportionalHazardsEstimator
label_desc: Use image node_id 1 for calling a global function with example usage: test_coxph_enum_num_interactions_orders(interaction_orders, fileName) without return types