code: def wilson_wrapper(y_true, y_pred, func):
      assert len(y_true) == len(y_pred)
      p = func(y_true, y_pred)
      n = len(y_true)
      result = compute_wilson_bounds(p, n, digits_of_precision, z_score)
      if float("nan") in result:
          return [None, None]
      return result
  
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 2 for calling a global function with example usage: wilson_wrapper(y_true, y_pred, func) and returns: result, list