code: def _average_binary_score(
      binary_metric, y_true, y_score, average, sample_weight=None
  ):
      """Average a binary metric for multilabel classification.
  
      Parameters
      ----------
      y_true : array, shape = [n_samples] or [n_samples, n_classes]
          True binary labels in binary label indicators.
  
      y_score : array, shape = [n_samples] or [n_samples, n_classes]
          Target scores, can either be probability estimates of the positive
          class, confidence values, or binary decisions.
  
      average : {None, 'micro', 'macro', 'samples', 'weighted'}, default='macro'
          If ``None``, the scores for each class are returned. Otherwise,
          this determines the type of averaging performed on the data:
  
          ``'micro'``:
              Calculate metrics globally by considering each element of the label
              indicator matrix as a label.
          ``'macro'``:
              Calculate metrics for each label, and find their unweighted
              mean.  This does not take label imbalance into account.
          ``'weighted'``:
              Calculate metrics for each label, and find their average, weighted
              by support (the number of true instances for each label).
          ``'samples'``:
              Calculate metrics for each instance, and find their average.
  
          Will be ignored when ``y_true`` is binary.
  
      sample_weight : array-like of shape (n_samples,), default=None
          Sample weights.
  
      binary_metric : callable, returns shape [n_classes]
          The binary metric function to use.
  
      Returns
      -------
      score : float or array of shape [n_classes]
          If not ``None``, average the score, else return the score for each
          classes.
  
      """
      average_options = (None, "micro", "macro", "weighted", "samples")
      if average not in average_options:
          raise ValueError(
              "average has to be one of {0}".format(average_options)
          )
  
      y_type = type_of_target(y_true)
      if y_type not in ("binary", "multilabel-indicator"):
          raise ValueError("{0} format is not supported".format(y_type))
  
      if y_type == "binary":
          return binary_metric(
              y_true, y_score, sample_weight=sample_weight
          )
  
      check_consistent_length(y_true, y_score, sample_weight)
      y_true = check_array(y_true)
      y_score = check_array(y_score)
  
      not_average_axis = 1
      score_weight = sample_weight
      average_weight = None
  
      if average == "micro":
          if score_weight is not None:
              score_weight = np.repeat(score_weight, y_true.shape[1])
          y_true = y_true.ravel()
          y_score = y_score.ravel()
  
      elif average == "weighted":
          if score_weight is not None:
              average_weight = np.sum(
                  np.multiply(
                      y_true, np.reshape(score_weight, (-1, 1))
                  ),
                  axis=0,
              )
          else:
              average_weight = np.sum(y_true, axis=0)
          if np.isclose(average_weight.sum(), 0.0):
              return 0
  
      elif average == "samples":
          # swap average_weight <-> score_weight
          average_weight = score_weight
          score_weight = None
          not_average_axis = 0
  
      if y_true.ndim == 1:
          y_true = y_true.reshape((-1, 1))
  
      if y_score.ndim == 1:
          y_score = y_score.reshape((-1, 1))
  
      n_classes = y_score.shape[not_average_axis]
      score = np.zeros((n_classes,))
      for c in range(n_classes):
          y_true_c = y_true.take([c], axis=not_average_axis).ravel()
          y_score_c = y_score.take([c], axis=not_average_axis).ravel()
          score[c] = binary_metric(
              y_true_c, y_score_c, sample_weight=score_weight
          )
  
      # Average the results
      if average is not None:
          if average_weight is not None:
              # Scores with 0 weights are forced to be 0, preventing the average
              # score from being affected by 0-weighted NaN elements.
              average_weight = np.asarray(average_weight)
              score[average_weight == 0] = 0
          return np.average(score, weights=average_weight)
      else:
          return score
  
imports: from itertools import combinations
  import numpy
  from ..utils import check_array, check_consistent_length
  from ..utils.multiclass import type_of_target
label_desc: Use image node_id 1 for calling a global function with example usage: _average_binary_score(binary_metric, y_true, y_score, average, sample_weight) and returns: binary_metric, np, score, int