code: def calc_errors(detections, annotations, matches=None):
      """
      Errors of the detections to the closest annotations.
  
      Parameters
      ----------
      detections : list or numpy array
          Detected events.
      annotations : list or numpy array
          Annotated events.
      matches : list or numpy array
          Indices of the closest events.
  
      Returns
      -------
      errors : numpy array
          Errors.
  
      Notes
      -----
      The sequences must be ordered. To speed up the calculation, a list of
      pre-computed indices of the closest matches can be used.
  
      """
      # make sure the arrays have the correct types
      detections = np.asarray(detections, dtype=float)
      annotations = np.asarray(annotations, dtype=float)
      if matches is not None:
          matches = np.asarray(matches, dtype=int)
      # TODO: right now, it only works with 1D arrays
      if detections.ndim > 1 or annotations.ndim > 1:
          raise NotImplementedError(
              "please implement multi-dim support"
          )
      # if no detections or annotations are given
      if len(detections) == 0 or len(annotations) == 0:
          # return a empty array
          return np.zeros(0, dtype=float)
      # determine the closest annotations
      if matches is None:
          matches = find_closest_matches(detections, annotations)
      # calc error relative to those annotations
      errors = detections - annotations[matches]
      # return the errors
      return errors
  
imports: from __future__ import absolute_import, division, print_function
  import numpy
  from .None import chords, beats, notes, onsets, tempo
  from .beats import BeatEvaluation, BeatMeanEvaluation
  from .chords import ChordEvaluation, ChordMeanEvaluation, ChordSumEvaluation
  from .key import KeyEvaluation, KeyMeanEvaluation
  from .notes import NoteEvaluation, NoteMeanEvaluation, NoteSumEvaluation
  from .onsets import OnsetEvaluation, OnsetMeanEvaluation, OnsetSumEvaluation
  from .tempo import TempoEvaluation, TempoMeanEvaluation
label_desc: Use image node_id 2 for calling a global function with example usage: calc_errors(detections, annotations, matches) and returns: errors, np