code: def intersection(
      func,
      y_true,
      y_pred=None,
      prot_attr=None,
      sample_weight=None,
      return_groups=False,
      **kwargs,
  ):
      """Compute an arbitrary metric on all intersectional groups of the protected
      attributes provided.
  
      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.
          sample_weight (array-like, optional): Sample weights passed through to
              func.
          return_groups (bool, optional): 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 for each intersectional group.
  
          tuple:
              Metric values and their corresponding group names.
  
              * **vals** (`list`) -- List of metric values for each intersectional
                group
              * **groups** (:class:`numpy.ndarray`) -- Array of tuples containing
                unique intersectional groups derived from the provided protected
                attributes.
  
      Examples:
          >>> X, y = fetch_german()
          >>> v, k = intersection(base_rate, y, prot_attr=['sex', 'age'],
          ...                     return_groups=True, pos_label='good')
          >>> dict(zip(k, v))
          {('female', 'aged'): 0.697560975609756,
           ('female', 'young'): 0.5523809523809524,
           ('male', 'aged'): 0.7388429752066116,
           ('male', 'young'): 0.611764705882353}
      """
      groups, _ = check_groups(y_true, prot_attr)
      unique_groups = np.unique(groups)
      func_vals = []
      for g in unique_groups:
          idx = groups == g
          sub = [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)
              func_vals.append(
                  func(*sub, sample_weight=sample_weight[idx], **kwargs)
              )
          else:
              func_vals.append(func(*sub, **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 3 for calling a global function with example usage: intersection(func, y_true, y_pred, prot_attr, sample_weight, return_groups) and returns: func_vals, func_vals,  unique_groups