code: def test_fold_column_is_used_properly_in_mojo():
      train = h2o.import_file(
          pu.locate("smalldata/iris/iris_train.csv")
      )
      test = h2o.import_file(pu.locate("smalldata/iris/iris_train.csv"))
      x = train.columns
      y = "petal_wid"
      x.remove(y)
      train["fold_col"] = h2o.H2OFrame(
          [i % 5 for i in range(train.nrow)]
      )
      test["fold_col"] = h2o.H2OFrame([i % 5 for i in range(test.nrow)])
  
      dl = H2ODeepLearningEstimator(
          keep_cross_validation_predictions=True, fold_column="fold_col"
      )
      dl.train(x=x, y=y, training_frame=train)
  
      drf = H2ORandomForestEstimator(
          keep_cross_validation_predictions=True, fold_column="fold_col"
      )
      drf.train(x=x, y=y, training_frame=train)
  
      gbm = H2OGradientBoostingEstimator(
          keep_cross_validation_predictions=True, fold_column="fold_col"
      )
      gbm.train(x=x, y=y, training_frame=train)
  
      glm = H2OGeneralizedLinearEstimator(
          keep_cross_validation_predictions=True, fold_column="fold_col"
      )
      glm.train(x=x, y=y, training_frame=train)
  
      se = H2OStackedEnsembleEstimator(
          training_frame=train,
          base_models=[gbm, drf, dl],
          metalearner_fold_column="fold_col",
      )
      se.train(x=x, y=y, training_frame=train)
  
      try:
          tempdir = tempfile.mkdtemp()
          predictions = se.predict(test)
          mojoname = se.save_mojo(tempdir)
          mojo_model = h2o.import_mojo(mojoname)
          try:
              mojo_predictions1 = mojo_model.predict(test)
          except Exception:
              assert (
                  False
              ), "Can't use the SE loaded from mojo to predict with the whole dataset including the fold_column"
          try:
              mojo_predictions2 = mojo_model.predict(
                  test[x + [y]]
              )  # without the fold column present
          except Exception:
              assert (
                  False
              ), "Can't use the SE loaded from mojo to predict with the whole dataset without the fold_column"
          assert (predictions == mojo_predictions1).all()
          assert (predictions == mojo_predictions2).all()
      finally:
          shutil.rmtree(tempdir)
  
imports: import shutil
  import tempfile
  import h2o
  import sys
  import warnings
  from h2o.estimators import *
  from tests import pyunit_utils
label_desc: Use image node_id 1 for calling a global function with example usage: test_fold_column_is_used_properly_in_mojo() without return types