code: def cv_nfolds_gbm_fold_assignment():
      prostate = h2o.import_file(
          path=pyunit_utils.locate("smalldata/logreg/prostate.csv")
      )
      prostate[1] = prostate[1].asfactor()
      prostate.summary()
  
      prostate_gbm = H2OGradientBoostingEstimator(
          nfolds=5,
          distribution="bernoulli",
          keep_cross_validation_models=True,
          keep_cross_validation_predictions=True,
          keep_cross_validation_fold_assignment=True,
      )
      prostate_gbm.train(
          x=list(range(2, 9)), y=1, training_frame=prostate
      )
      prostate_gbm.cross_validation_fold_assignment().describe()
      prostate_gbm.cross_validation_holdout_predictions().describe()
      for m in prostate_gbm.cross_validation_predictions():
          m.describe()
      for m in prostate_gbm.cross_validation_models():
          m.show()
  
imports: import sys
  import h2o
  from tests import pyunit_utils
  from h2o.estimators.gbm import H2OGradientBoostingEstimator
label_desc: Use image node_id 1 for calling a global function with example usage: cv_nfolds_gbm_fold_assignment() without return types