code: def test_isotonic_regression_nfolds():
      X, y = make_regression(
          n_samples=10000, n_features=1, random_state=41, noise=0.8
      )
      X = X.reshape(-1)
  
      train = H2OFrame(np.column_stack((y, X)), column_names=["y", "X"])
  
      h2o_iso_reg = H2OIsotonicRegressionEstimator(
          training_frame=train, nfolds=2, out_of_bounds="clip"
      )
      h2o_iso_reg.train(x="X", y="y")
  
      print(h2o_iso_reg.model_performance())
      print(h2o_iso_reg.model_performance(xval=True))
      assert h2o_iso_reg.model_performance(xval=True)
  
imports: import sys
  from tests import pyunit_utils
  import numpy
  from sklearn.datasets import make_regression
  from h2o import H2OFrame
  from h2o.estimators.isotonicregression import H2OIsotonicRegressionEstimator
label_desc: Use image node_id 1 for calling a global function with example usage: test_isotonic_regression_nfolds() without return types