code: def coxph_smoke():
      rossi = load_rossi()
  
      cph = CoxPHFitter()
      cph.fit(rossi, duration_col="week", event_col="arrest")
  
      cph.print_summary()
  
      rossiH2O = h2o.H2OFrame(rossi)
      cphH2O = H2OCoxProportionalHazardsEstimator(stop_column="week")
      cphH2O.train(
          x=["age", "fin", "race", "wexp", "mar", "paro", "prio"],
          y="arrest",
          training_frame=rossiH2O,
      )
  
      assert cphH2O.model_id != ""
      assert (
          cphH2O.formula()
          == "Surv(week, arrest) ~ fin + age + race + wexp + mar + paro + prio"
      ), (
          "Expected formula to be 'Surv(week, arrest) ~ fin + age + race + wexp + mar + paro + prio' but it was "
          + cphH2O.formula()
      )
  
      predH2O = cphH2O.predict(
          test_data=rossiH2O
      )  # the same dataset as training
      assert len(predH2O) == len(rossi)
  
      metricsH2O = cphH2O.model_performance(rossiH2O)
      assert (
          abs(metricsH2O.concordance() - cphH2O.concordance()) < 0.001
      )
  
      py_concordance = concordance_for_lifelines(cph)
      assert abs(py_concordance - metricsH2O.concordance()) < 0.001
  
imports: import sys
  import h2o
  from lifelines import CoxPHFitter
  from lifelines.datasets import load_rossi
  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: coxph_smoke() without return types