code: def test_weights_column():
      print("Check weights_column")
      ds = import_dataset()
      nrows = ds.train.nrows
      weights_column = "weight"
      train = ds.train.concat(
          h2o.H2OFrame(
              list(map(lambda _: uniform(0, 5), range(nrows))),
              column_names=[weights_column],
          )
      )
      aml = H2OAutoML(
          project_name="py_aml_weights_column", max_models=3, seed=1
      )
      aml.train(
          y=ds.target,
          training_frame=train,
          weights_column=weights_column,
      )
      base_models = get_partitioned_model_names(aml.leaderboard).base
      amodel = h2o.get_model(base_models[0])
      assert (
          amodel.params["weights_column"]["actual"]["column_name"]
          == weights_column
      )
  
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 4 for calling a global function with example usage: test_weights_column() without return types