code: def difference(
      func,
      y_true,
      y_pred=None,
      prot_attr=None,
      priv_group=1,
      sample_weight=None,
      **kwargs,
  ):
      """Compute the difference between unprivileged and privileged subsets for an
      arbitrary metric.
  
      Note: The optimal value of a difference is 0. To make it a scorer, one must
      take the absolute value and set greater_is_better to False.
  
      Unprivileged group is taken to be the inverse of the privileged group.
  
      Args:
          func (function): A metric function from :mod:`sklearn.metrics` or
              :mod:`aif360.sklearn.metrics`.
          y_true (pandas.Series): Outcome vector with protected attributes as
              index.
          y_pred (array-like, optional): Estimated outcomes.
          prot_attr (array-like, keyword-only): Protected attribute(s). If
              ``None``, all protected attributes in y are used.
          priv_group (scalar, optional): The label of the privileged group.
          sample_weight (array-like, optional): Sample weights passed through to
              func.
          **kwargs: Additional keyword args to be passed through to func.
  
      Returns:
          scalar: Difference in metric value for unprivileged and privileged
          groups.
  
      Examples:
          >>> X, y = fetch_german(numeric_only=True)
          >>> y_pred = LogisticRegression().fit(X, y).predict(X)
          >>> difference(precision_score, y, y_pred, prot_attr='sex',
          ... priv_group='male')
          -0.06955430006277463
      """
      groups, _ = check_groups(y_true, prot_attr)
      idx = groups == priv_group
      unpriv = [y[~idx] for y in (y_true, y_pred) if y is not None]
      priv = [y[idx] for y in (y_true, y_pred) if y is not None]
      if sample_weight is not None:
          sample_weight = np.asarray(sample_weight)
          return func(
              *unpriv, sample_weight=sample_weight[~idx], **kwargs
          ) - func(*priv, sample_weight=sample_weight[idx], **kwargs)
      return func(*unpriv, **kwargs) - func(*priv, **kwargs)
  
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 1 for calling a global function with example usage: difference(func, y_true, y_pred, prot_attr, priv_group, sample_weight) and returns: unknown, unknown