code: def CER(prediction, true_labels):
    
  
    errors = (prediction != true_labels).sum()
    return float(errors)/len(prediction)
docs: Calculates the classification error rate for an N-class classification problem
  
  
    Parameters:
  
    prediction (numpy.ndarray): A 1D :py:class:`numpy.ndarray` containing your
      prediction
  
    true_labels (numpy.ndarray): A 1D :py:class:`numpy.ndarray`
      containing the ground truth labels for the input array, organized in the
      same order.