code: def test_infogram_personal_loan_cv_fold_column():
      """
      Make sure safe infogram works with validation frame and supports cross-validation
      """
      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",
      ]
      n_fold = 3
      infogram_model_cv = H2OInfogram(
          seed=12345,
          protected_columns=["Age", "ZIP Code"],
          nfolds=n_fold,
          fold_assignment="modulo",
      )
      infogram_model_cv.train(
          x=x, y=target, training_frame=fr
      )  # model with cross-vdaliation
  
      fold_numbers = fr.modulo_kfold_column(n_folds=n_fold)
      fold_numbers.set_names(["fold_numbers"])
      fr = fr.cbind(fold_numbers)
  
      infogram_model_cv_fold_column = H2OInfogram(
          seed=12345,
          protected_columns=["Age", "ZIP Code"],
          fold_column="fold_numbers",
      )
      infogram_model_cv_fold_column.train(
          x=x, y=target, training_frame=fr
      )  # cross-validation, validation
  
      print("compare rel cmi from training dataset")
      relcmi_train_cv = infogram_model_cv.get_admissible_score_frame()
      relcmi_train_cv_fold_column = (
          infogram_model_cv_fold_column.get_admissible_score_frame()
      )
      pyunit_utils.compare_frames_local(
          relcmi_train_cv, relcmi_train_cv_fold_column, prob=1.0
      )
  
      print("compare rel cmi from cross-validation hold out")
      relcmi_cv_cv = infogram_model_cv.get_admissible_score_frame(
          xval=True
      )
      relcmi_cv_cv_fold_column = (
          infogram_model_cv_fold_column.get_admissible_score_frame(
              xval=True
          )
      )
      pyunit_utils.compare_frames_local(
          relcmi_cv_cv, relcmi_cv_cv_fold_column, prob=1.0
      )
  
imports: 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_infogram_personal_loan_cv_fold_column() without return types