class_docstr: Equalized odds postprocessing is a post-processing technique that solves
  a linear program to find probabilities with which to change output labels to
  optimize equalized odds [8]_ [9]_.
  
  References:
      .. [8] M. Hardt, E. Price, and N. Srebro, "Equality of Opportunity in
         Supervised Learning," Conference on Neural Information Processing
         Systems, 2016.
      .. [9] G. Pleiss, M. Raghavan, F. Wu, J. Kleinberg, and
         K. Q. Weinberger, "On Fairness and Calibration," Conference on Neural
         Information Processing Systems, 2017.
docstr: fit and predict methods sequentially.
code: def fit_predict(self, dataset_true, dataset_pred):
      """fit and predict methods sequentially."""
      return self.fit(dataset_true, dataset_pred).predict(dataset_pred)
  
file_path: AIF360/aif360/algorithms/postprocessing/eq_odds_postprocessing.py
imports: import numpy
  from scipy.optimize import linprog
  from aif360.algorithms import Transformer
  from aif360.metrics import ClassificationMetric, utils
label_desc: Use image node_id 4 for calling the EqOddsPostprocessing obj's underlying member method code with example usage: obj.fit_predict(dataset_true, dataset_pred) and returns: self