code: def mae_standard_normal(y_true, y_pred):
      assert len(y_true) == len(y_pred)
      absolute_error = np.abs(y_true - y_pred)
      sample_std = np.std(absolute_error)
      mae = np.mean(absolute_error)
      standard_error = z_score * sample_std / np.sqrt(len(y_true))
      return mae - standard_error, mae + standard_error
  
file_path: responsible-ai-toolbox/raiwidgets/raiwidgets/fairness_metric_calculation.py
imports: import importlib
  import numpy
  from packaging import version
  from sklearn.metrics import confusion_matrix
label_desc: Use image node_id 10 for calling a global function with example usage: mae_standard_normal(y_true, y_pred) and returns: unknown,  unknown