code: def _compute(
      self, predictions=None, references=None, concatenate_texts=False
  ):
      if concatenate_texts:
          return compute_measures(references, predictions)["wer"]
      else:
          incorrect = 0
          total = 0
          max = 0
          id = 0
          id_max = 0
          for prediction, reference in zip(predictions, references):
              measures = compute_measures(reference, prediction)
              if (
                  measures["substitutions"]
                  + measures["deletions"]
                  + measures["insertions"]
                  > max
              ):
                  max = (
                      measures["substitutions"]
                      + measures["deletions"]
                      + measures["insertions"]
                  )
                  id_max = id
              incorrect += (
                  measures["substitutions"]
                  + measures["deletions"]
                  + measures["insertions"]
              )
              total += (
                  measures["substitutions"]
                  + measures["deletions"]
                  + measures["hits"]
              )
              id += 1
          print(id_max, max)
          print(predictions[id_max])
          print(references[id_max])
          return incorrect / total
  
imports: import datasets
  from jiwer import compute_measures
  import evaluate
label_desc: Use image node_id 2 for calling the WER obj's underlying member method code with example usage: obj._compute(predictions, references, concatenate_texts) and returns: compute_measures, unknown