class_docstr: Calibrated equalized odds postprocessing is a post-processing technique
  that optimizes over calibrated classifier score outputs to find
  probabilities with which to change output labels with an equalized odds
  objective [7]_.
  
  References:
      .. [7] G. Pleiss, M. Raghavan, F. Wu, J. Kleinberg, and
         K. Q. Weinberger, "On Fairness and Calibration," Conference on Neural
         Information Processing Systems, 2017
  
  Adapted from:
  https://github.com/gpleiss/equalized_odds_and_calibration/blob/master/calib_eq_odds.py
docstr: fit and predict methods sequentially.
code: def fit_predict(self, dataset_true, dataset_pred, threshold=0.5):
      """fit and predict methods sequentially."""
      return self.fit(dataset_true, dataset_pred).predict(
          dataset_pred, threshold=threshold
      )
  
file_path: AIF360/aif360/algorithms/postprocessing/calibrated_eq_odds_postprocessing.py
imports: import numpy
  from aif360.algorithms import Transformer
  from aif360.metrics import ClassificationMetric, utils
label_desc: Use image node_id 4 for calling the CalibratedEqOddsPostprocessing obj's underlying member method code with example usage: obj.fit_predict(dataset_true, dataset_pred, threshold) and returns: self