code: def _fbeta_score(precision, recall, beta):
      beta_square = beta * beta
      return (
          (1 + beta_square)
          * precision
          * recall
          / (beta_square * precision + recall)
      ).astype(precision.dtype, copy=False)
  
file_path: chainer/chainer/functions/evaluation/classification_summary.py
imports: from __future__ import division
  import six
  import chainer
  from chainer import backend
  from chainer import function
  from chainer.utils import type_check
label_desc: Use image node_id 1 for calling a global function with example usage: _fbeta_score(precision, recall, beta) and returns: unknown