code: def test_cv_warning_messages():
      """
      In this test, I will enable cv with a validation dataset.  There should not be warning on weights because the
      training set does not contains weight column even though cv is enabled.
      """
      fr = h2o.import_file(
          path=pyunit_utils.locate(
              "smalldata/admissibleml_test/Bank_Personal_Loan_Modelling.csv"
          )
      )
      target = "Personal Loan"
      fr[target] = fr[target].asfactor()
      x = [
          "Experience",
          "Income",
          "Family",
          "CCAvg",
          "Education",
          "Mortgage",
          "Securities Account",
          "CD Account",
          "Online",
          "CreditCard",
      ]
      splits = fr.split_frame(ratios=[0.80])
      train = splits[0]
      test = splits[1]
      infogram_model_cv_v = H2OInfogram(
          seed=12345, protected_columns=["Age", "ZIP Code"], nfolds=3
      )
      infogram_model_cv_v.train(
          x=x, y=target, training_frame=train, validation_frame=test
      )
  
      pyunit_utils.checkLogWeightWarning(
          "infogram_internal_cv_weights_", wantWarnMessage=False
      )
  
imports: from __future__ import print_function
  import os
  import sys
  import h2o
  from h2o.estimators.infogram import H2OInfogram
  from tests import pyunit_utils
label_desc: Use image node_id 1 for calling a global function with example usage: test_cv_warning_messages() without return types