code: def _calibrate(self, calibration_model):
      """
      Calibrate a trained model with a supplied calibration model.
  
      Only tree-based models can be calibrated.
  
      :param calibration_model: a GLM model (for Platt Scaling) or Isotonic Regression model trained with the purpose
          of calibrating output of this model.
  
      :examples:
  
      >>> from h2o.estimators.gbm import H2OGradientBoostingEstimator
      >>> from h2o.estimators.isotonicregression import H2OIsotonicRegressionEstimator
      >>> df = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/gbm_test/ecology_model.csv")
      >>> df["Angaus"] = df["Angaus"].asfactor()
      >>> train, calib = df.split_frame(ratios=[.8], destination_frames=["eco_train", "eco_calib"], seed=42)
      >>> model = H2OGradientBoostingEstimator()
      >>> model.train(x=list(range(2, train.ncol)), y="Angaus", training_frame=train)
      >>> isotonic_train = calib[["Angaus"]]
      >>> isotonic_train = isotonic_train.cbind(model.predict(calib)["p1"])
      >>> h2o_iso_reg = H2OIsotonicRegressionEstimator(out_of_bounds="clip")
      >>> h2o_iso_reg.train(training_frame=isotonic_train, x="p1", y="Angaus")
      >>> model.calibrate(h2o_iso_reg)
      >>> model.predict(train)
      """
      from h2o.expr import ExprNode
  
      result = (
          ExprNode("set.calibration.model", self, calibration_model)
          ._eval_driver("scalar")
          ._cache._data
      )
      if result != "OK":
          warn(result)
  
label_desc: Use image node_id 2 for calling the SupervisedTrees obj's underlying member method code with example usage: obj._calibrate(calibration_model) without return types