code: def test_infogram_breast_cancer_cv_fold_column():
      """
      Test to make sure cross-validation are implemented properly using fold_column
      """
      fr = h2o.import_file(
          path=pyunit_utils.locate(
              "smalldata/admissibleml_test/wdbc_changed.csv"
          )
      )
      target = "diagnosis"
      fr[target] = fr[target].asfactor()
  
      x = [
          "radius_mean",
          "texture_mean",
          "perimeter_mean",
          "area_mean",
          "smoothness_mean",
          "compactness_mean",
          "concavity_mean",
          "concave_points_mean",
          "symmetry_mean",
          "fractal_dimension_mean",
          "radius_se",
          "texture_se",
          "perimeter_se",
          "area_se",
          "smoothness_se",
          "compactness_se",
          "concavity_se",
          "concave_points_se",
          "symmetry_se",
          "fractal_dimension_se",
          "radius_worst",
          "texture_worst",
          "perimeter_worst",
          "area_worst",
          "smoothness_worst",
          "compactness_worst",
          "concavity_worst",
          "concave_points_worst",
          "symmetry_worst",
          "fractal_dimension_worst",
      ]
      splits = fr.split_frame(ratios=[0.80])
      train = splits[0]
      test = splits[1]
      n_fold = 3
      infogram_model_cv_valid = H2OInfogram(
          seed=12345,
          top_n_features=50,
          nfolds=n_fold,
          fold_assignment="modulo",
      )  # model with cross-validation
      infogram_model_cv_valid.train(
          x=x, y=target, training_frame=train, validation_frame=test
      )
      relcmi_train_cv_valid = (
          infogram_model_cv_valid.get_admissible_score_frame()
      )
      relcmi_cv_cv_valid = (
          infogram_model_cv_valid.get_admissible_score_frame(xval=True)
      )
      relcmi_valid_cv_valid = (
          infogram_model_cv_valid.get_admissible_score_frame(valid=True)
      )
  
      infogram_model = H2OInfogram(seed=12345, top_n_features=50)
      infogram_model.train(x=x, y=target, training_frame=train)
      relcmi_train = infogram_model.get_admissible_score_frame()
  
      infogram_model_valid = H2OInfogram(seed=12345, top_n_features=50)
      infogram_model_valid.train(
          x=x, y=target, training_frame=train, validation_frame=test
      )
      relcmi_train_valid = (
          infogram_model_valid.get_admissible_score_frame()
      )
      relcmi_valid_valid = (
          infogram_model_valid.get_admissible_score_frame(valid=True)
      )
  
      infogram_model_cv = H2OInfogram(
          seed=12345,
          top_n_features=50,
          nfolds=n_fold,
          fold_assignment="modulo",
      )
      infogram_model_cv.train(x=x, y=target, training_frame=train)
      relcmi_train_cv = infogram_model_cv.get_admissible_score_frame()
      relcmi_cv_cv = infogram_model_cv.get_admissible_score_frame(
          xval=True
      )
  
      # training rel cmi frames should all equal
      print("Comparing infogram data from training dataset")
      pyunit_utils.compare_frames_local(
          relcmi_train_cv_valid, relcmi_train, prob=1
      )
      pyunit_utils.compare_frames_local(
          relcmi_train_cv, relcmi_train_valid, prob=1
      )
      pyunit_utils.compare_frames_local(
          relcmi_train_cv_valid, relcmi_train_cv, prob=1
      )
  
      # valid rel cmi frames should be the same
      print("Comparing infogram data from validation dataset")
      pyunit_utils.compare_frames_local(
          relcmi_valid_cv_valid, relcmi_valid_valid, prob=1
      )
  
      print("Comparing infogram data from cross-validation dataset")
      pyunit_utils.compare_frames_local(
          relcmi_cv_cv, relcmi_cv_cv_valid, prob=1
      )
  
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_breast_cancer_cv_fold_column() without return types