code: def char_error_rate(
      preds: Union[str, List[str]], target: Union[str, List[str]]
  ) -> Tensor:
      """Compute Character Error Rate used for performance of an automatic speech recognition system.
  
      This value indicates the percentage of characters that were incorrectly predicted. The lower the value, the better
      the performance of the ASR system with a CER of 0 being a perfect score.
  
      Args:
          preds: Transcription(s) to score as a string or list of strings
          target: Reference(s) for each speech input as a string or list of strings
  
      Returns:
          Character error rate score
  
      Examples:
          >>> preds = ["this is the prediction", "there is an other sample"]
          >>> target = ["this is the reference", "there is another one"]
          >>> char_error_rate(preds=preds, target=target)
          tensor(0.3415)
  
      """
      errors, total = _cer_update(preds, target)
      return _cer_compute(errors, total)
  
imports: from typing import List, Tuple, Union
  import torch
  from torch import Tensor, tensor
  from torchmetrics.functional.text.helper import _edit_distance
label_desc: Use image node_id 3 for calling a global function with example usage: char_error_rate(preds, target) and returns: _cer_compute