code: def evaluate(reference_key, estimated_key, **kwargs):
      
      # Compute all metrics
      scores = collections.OrderedDict()
  
      scores['Weighted Score'] = util.filter_kwargs(
              weighted_score, reference_key, estimated_key)
  
      return scores
docs: Compute all metrics for the given reference and estimated annotations.
  
      Examples
      --------
      >>> ref_key = mir_eval.io.load_key('reference.txt')
      >>> est_key = mir_eval.io.load_key('estimated.txt')
      >>> scores = mir_eval.key.evaluate(ref_key, est_key)
  
      Parameters
      ----------
      ref_key : str
          Reference key string.
  
      ref_key : str
          Estimated key string.
  
      kwargs
          Additional keyword arguments which will be passed to the
          appropriate metric or preprocessing functions.
  
      Returns
      -------
      scores : dict
          Dictionary of scores, where the key is the metric name (str) and
          the value is the (float) score achieved.