code: def test_suite_stackedensemble_binary_model(blending=False):
      def test_saved_binary_model_produces_same_predictions_as_original():
          ds = prepare_data(blending)
          base_models = train_base_models(ds)
          se_model = train_stacked_ensemble(ds, base_models)
  
          # Predict in ensemble in Py client
          preds_py = se_model.predict(ds.test)
  
          tmp_dir = tempfile.mkdtemp()
          try:
              bin_file = h2o.save_model(se_model, tmp_dir)
              # Load binary model and predict
              bin_model = h2o.load_model(pu.locate(bin_file))
              preds_bin = bin_model.predict(ds.test)
          finally:
              shutil.rmtree(tmp_dir)
  
          # Predictions from model in Py and binary model should be the same
          pred_diff = preds_bin - preds_py
          assert pred_diff["p0"].max() < 1e-11
          assert pred_diff["p1"].max() < 1e-11
          assert pred_diff["p0"].min() > -1e-11
          assert pred_diff["p1"].min() > -1e-11
  
      return [
          pu.tag_test(test, "blending" if blending else None)
          for test in [
              test_saved_binary_model_produces_same_predictions_as_original
          ]
      ]
  
file_path: h2o-3/h2o-py/tests/testdir_algos/stackedensemble/pyunit_stackedensemble_binary.py
imports: import h2o
  import tempfile
  import shutil
  import sys
  from h2o.estimators.random_forest import H2ORandomForestEstimator
  from h2o.estimators.gbm import H2OGradientBoostingEstimator
  from h2o.estimators.stackedensemble import H2OStackedEnsembleEstimator
  from tests import pyunit_utils
label_desc: Use image node_id 4 for calling a global function with example usage: test_suite_stackedensemble_binary_model(blending) and returns: unknown