code: def generalized_entropy_error(y_true, y_pred, alpha=2, pos_label=1):
      #                           sample_weight=None):
      r"""Compute the generalized entropy.
  
      Generalized entropy index is proposed as a unified individual and
      group fairness measure in [#speicher18]_.
  
      Uses :math:`b_i = \hat{y}_i - y_i + 1`. See
      :func:`generalized_entropy_index` for details.
  
      Args:
          y_true (array-like): Ground truth (correct) target values.
          y_pred (array-like): Estimated targets as returned by a classifier.
          alpha (scalar, optional): Parameter that regulates the weight given to
              distances between values at different parts of the distribution. A
              value of 0 is equivalent to the mean log deviation, 1 is the Theil
              index, and 2 is half the squared coefficient of variation.
          pos_label (scalar, optional): The label of the positive class.
  
      See also:
          :func:`generalized_entropy_index`
  
      References:
          .. [#speicher18] `T. Speicher, H. Heidari, N. Grgic-Hlaca,
             K. P. Gummadi, A. Singla, A. Weller, and M. B. Zafar, "A Unified
             Approach to Quantifying Algorithmic Unfairness: Measuring Individual
             and Group Unfairness via Inequality Indices," ACM SIGKDD
             International Conference on Knowledge Discovery and Data Mining,
             2018. <https://dl.acm.org/citation.cfm?id=3220046>`_
      """
      b = 1 + (y_pred == pos_label) - (y_true == pos_label)
      return generalized_entropy_index(b, alpha=alpha)
  
imports: from itertools import permutations
  from typing import Union
  import warnings
  import numpy
  import pandas
  from scipy.special import rel_entr
  from sklearn.metrics import make_scorer, recall_score, precision_score
  from sklearn.metrics import multilabel_confusion_matrix
  from sklearn.metrics._classification import _prf_divide, _check_zero_division
  from sklearn.neighbors import NearestNeighbors
  from sklearn.utils import check_X_y
  from aif360.metrics import ot_metric
  from aif360.sklearn.utils import check_inputs, check_groups
  from aif360.detectors.mdss.ScoringFunctions import BerkJones, Bernoulli
  from aif360.detectors.mdss.MDSS import MDSS
label_desc: Use image node_id 30 for calling a global function with example usage: generalized_entropy_error(y_true, y_pred, alpha, pos_label) and returns: generalized_entropy_index