code: def selection_rate(
      y_true, y_pred, *, pos_label=1, sample_weight=None
  ):
      r"""Compute the selection rate, :math:`Pr(\hat{Y} = \text{pos_label}) =
      \frac{TP + FP}{P + N}`.
  
      Args:
          y_true (array-like): Ground truth (correct) target values. Ignored.
          y_pred (array-like): Estimated targets as returned by a classifier.
          pos_label (scalar, optional): The label of the positive class.
          sample_weight (array-like, optional): Sample weights.
  
      Returns:
          float: Selection rate.
      """
      return base_rate(
          y_pred, pos_label=pos_label, sample_weight=sample_weight
      )
  
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 11 for calling a global function with example usage: selection_rate(y_true, y_pred) and returns: base_rate