code: def calculate_report(labels, decodings, distances, losses):
      r'''
      This routine will calculate a WER report.
      It'll compute the `mean` WER and create ``Sample`` objects of the ``report_count`` top lowest
      loss items from the provided WER results tuple (only items with WER!=0 and ordered by their WER).
      '''
      samples = pmap(process_decode_result, zip(labels, decodings, distances, losses))
  
      # Getting the WER and CER from the accumulated edit distances and lengths
      samples_wer, samples_cer = wer_cer_batch(labels, decodings)
  
      # Order the remaining items by their loss (lowest loss on top)
      samples.sort(key=lambda s: s.loss)
  
      # Then order by WER (highest WER on top)
      samples.sort(key=lambda s: s.wer, reverse=True)
  
      return samples_wer, samples_cer, samplef calculate_report(labels, decodings, distances, losses):
      r'''
      This routine will calculate a WER report.
      It'll compute the `mean` WER and create ``Sample`` objects of the ``report_count`` top lowest
      loss items from the provided WER results tuple (only items with WER!=0 and ordered by their WER).
      '''
      samples = pmap(process_decode_result, zip(labels, decodings, distances, losses))
  
      # Getting the WER and CER from the accumulated edit distances and lengths
      samples_wer, samples_cer = wer_cer_batch(labels, decodings)
  
      # Order the remaining items by their loss (lowest loss on top)
      samples.sort(key=lambda s: s.loss)
  
      # Then order by WER (highest WER on top)
      samples.sort(key=lambda s: s.wer, reverse=True)
  
      return samples_wer, samples_cer, samples
docs: r'''
      This routine will calculate a WER report.
      It'll compute the `mean` WER and create ``Sample`` objects of the ``report_count`` top lowest
      loss items from the provided WER results tuple (only items with WER!=0 and ordered by their WER).