code: def test_isotonic_regression_validation():
      X, y = make_regression(
          n_samples=10000, n_features=1, random_state=41, noise=0.8
      )
      X = X.reshape(-1)
  
      df = H2OFrame(np.column_stack((y, X)), column_names=["y", "X"])
      train, valid = df.split_frame(ratios=[0.2])
  
      h2o_iso_reg = H2OIsotonicRegressionEstimator(
          training_frame=train,
          validation_frame=valid,
          out_of_bounds="clip",
      )
      h2o_iso_reg.train(x="X", y="y")
  
      print(h2o_iso_reg.model_performance())
      print(h2o_iso_reg.model_performance(valid=True))
  
      valid_copy = H2OFrame(valid.as_data_frame())
      valid_copy_performance = h2o_iso_reg.model_performance(
          test_data=valid_copy
      )
      print(valid_copy_performance)
  
      pyunit_utils.assertEqualModelMetrics(
          h2o_iso_reg.model_performance(valid=True),
          valid_copy_performance,
      )
  
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_validation() without return types