code: def error_rate(y_true, y_pred, diff=None):
      """Compute error rate for classification and regression tasks.
  
      :param y_true: True labels.
      :type y_true: numpy.ndarray
      :param y_pred: Predicted values.
      :type y_pred: numpy.ndarray
      :param diff: Difference between true and predicted values.
      :type diff: numpy.ndarray
      :return: Error rate.
      :rtype: float
      """
      if diff is None:
          diff = abs(y_pred - y_true)
      total = len(diff)
      error = int(diff.sum())
      if total == 0:
          metric_value = 0
      else:
          metric_value = error / total
      return metric_value
  
file_path: responsible-ai-toolbox/erroranalysis/erroranalysis/_internal/metrics.py
imports: import numpy
  from sklearn.metrics import accuracy_score, confusion_matrix, f1_score, mean_absolute_error, mean_squared_error, median_absolute_error, precision_score, r2_score, recall_score
  from erroranalysis._internal.constants import Metrics, ModelTask, f1_metrics, precision_metrics, recall_metrics
label_desc: Use image node_id 12 for calling a global function with example usage: error_rate(y_true, y_pred, diff) and returns: metric_value