code: def test_fold_column():
      print("Check fold_column param")
      ds = import_dataset()
      fold_column = "fold_id"
      nrows = ds.train.nrows
      train = ds.train.concat(
          h2o.H2OFrame(
              list(islice(cycle(range(3)), 0, nrows)),
              column_names=[fold_column],
          )
      )
      aml = H2OAutoML(
          project_name="py_aml_fold_column",
          max_models=3,
          seed=1,
          keep_cross_validation_models=True,
      )
      aml.train(
          y=ds.target, training_frame=train, fold_column=fold_column
      )
      models = get_partitioned_model_names(aml.leaderboard)
      amodel = h2o.get_model(models.base[0])
      assert (
          amodel.params["fold_column"]["actual"]["column_name"]
          == fold_column
      )
      ensemble = h2o.get_model(models.se[0])
      metalearner = h2o.get_model(ensemble.metalearner()["name"])
      assert (
          metalearner.params["fold_column"]["actual"]["column_name"]
          == fold_column
      )
      assert len(metalearner.cross_validation_models()) == 3
  
file_path: h2o-3/h2o-py/tests/testdir_algos/automl/pyunit_automl_cross_validation.py
imports: import sys, os
  from itertools import cycle, islice
  from random import uniform
  import h2o
  from h2o.automl import H2OAutoML
  from tests import pyunit_utils
  from _automl_utils import import_dataset, get_partitioned_model_names
label_desc: Use image node_id 3 for calling a global function with example usage: test_fold_column() without return types