code: def _macro(true, pred, func, unique_labels):
      scores = []
      for lab in unique_labels:
          scores.append(
              func(
                  np.where(true != lab, 1, 0),
                  np.where(pred != lab, 1, 0),
              )
          )
      return float(sum(scores)) / len(scores)
  
imports: import numpy
  from mlxtend.evaluate.accuracy import accuracy_score
  from mlxtend.evaluate.confusion_matrix import confusion_matrix
label_desc: Use image node_id 2 for calling a global function with example usage: _macro(true, pred, func, unique_labels) and returns: unknown