code: def recall_at_precision(y_true, y_pred, precision):
      
      y_true, y_pred = _mask_value_nan(y_true, y_pred)
      precision, recall, _ = skm.precision_recall_curve(y_true, y_pred)
      return recall[np.searchsorted(precision - precision, 0)]
docs: Recall at a certain precision threshold
  
      Args:
        y_true: true labels
        y_pred: predicted labels
        precision: resired precision level at which where to compute the recall