code: def one_vs_rest(
      func,
      y_true,
      y_pred=None,
      prot_attr=None,
      return_groups=False,
      **kwargs,
  ):
      """Compute an arbitrary difference/ratio metric on all intersectional groups
      of the protected attributes provided in a one-vs-rest manner.
  
      Args:
          func (function): A difference or ratio metric function from
              :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.
          sample_weight (array-like, optional): Sample weights passed through to
              func.
          return_groups (bool): Return group names in addition to metric values.
              Names are tuples of protected attribute values.
          **kwargs: Additional keyword args to be passed through to func.
  
      Returns:
          list: List of metric values considering each intersectional group in
          turn as privileged and the rest as unprivileged.
  
          tuple:
              Metric values and their corresponding group names.
  
              * **vals** (`list`) -- List of metric values considering each
                group in turn as privileged and the rest as unprivileged.
              * **groups** (:class:`numpy.ndarray`) -- Array of tuples containing
                unique intersectional groups derived from the provided protected
                attributes.
  
      Examples:
          >>> X, y = fetch_german()
          >>> v, k = one_vs_rest(statistical_parity_difference, y,
          ...                    prot_attr=['sex', 'age'], return_groups=True,
          ...                    pos_label='good')
          >>> dict(zip(k, v))
          {(0, 0): 0.16493748337323755,
           (0, 1): 0.0030679552078539674,
           (1, 0): 0.09643201542912239,
           (1, 1): -0.09833664609268755}
  
          >>> from functools import partial
          >>> from sklearn.metrics import accuracy_score
          >>> from sklearn.linear_model import LogisticRegression
          >>> y_pred = LogisticRegression(solver='liblinear').fit(X, y).predict(X)
          >>> acc_diff = partial(difference, accuracy_score)
          >>> one_vs_rest(acc_diff, y, y_pred, prot_attr=['sex', 'age'])
          [0.11338121840915127,
           -0.013775118883264326,
           0.018450658952105403,
           -0.04119677790563869]
      """
      groups, _ = check_groups(y_true, prot_attr)
      unique_groups = np.unique(groups)
      func_vals = []
      for g in unique_groups:
          func_vals.append(
              func(
                  y_true,
                  y_pred,
                  prot_attr=prot_attr,
                  priv_group=g,
                  **kwargs,
              )
          )
      if return_groups:
          return func_vals, unique_groups
      return func_vals
  
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 4 for calling a global function with example usage: one_vs_rest(func, y_true, y_pred, prot_attr, return_groups) and returns: func_vals, func_vals,  unique_groups