docs: def fbeta_score(
      gold, pred, pos_label=1, beta=1.0, ignore_in_gold=[], ignore_in_pred=[]
  ):
      """
      Calculate recall for a single class.
      Args:
          gold: A 1d array-like of gold labels
          pred: A 1d array-like of predicted labels (assuming abstain = 0)
          ignore_in_gold: A list of labels for which elements having that gold
              label will be ignored.
          ignore_in_pred: A list of labels for which elements having that pred
              label will be ignored.
          pos_label: The class label to treat as positive for f-beta
          beta: The beta to use in the f-beta score calculation
  
      Returns:
          fbeta: The (float) f-beta score
      """
      gold, pred = _preprocess(gold, pred, ignore_in_gold, ignore_in_pred)
      pre = precision_score(gold, pred, pos_label=pos_label)
      rec = recall_score(gold, pred, pos_label=pos_label)
  
      if pre or rec:
          fbeta = (1 + beta ** 2) * (pre * rec) / ((beta ** 2 * pre) + rec)
      else:
          fbeta = 0
  
      return fbeta