code: def test_coxph_mojo_with_stratification_and_categoricals():
      data = h2o.create_frame(
          categorical_fraction=1,
          missing_fraction=0,
          factors=10,
          has_response=True,
          seed=10,
      )
      data["C1"] = data["C1"].asnumeric()
      print(data)
  
      model = H2OCoxProportionalHazardsEstimator(
          stratify_by=["C2", "C3"], stop_column="C1"
      )
      model.train(y="response", training_frame=data)
      print(model)
  
      # reference predictions
      h2o_prediction = model.predict(data)
  
      assert pyunit_utils.test_java_scoring(
          model, data, h2o_prediction, 1e-8
      )
  
imports: import sys
  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_mojo_with_stratification_and_categoricals() without return types