code: def test_roc_auc():
      ras = functools.partial(
          skm.roc_auc_score, multi_class="ovr", labels=[0, 1, 2]
      )
      ras.__name__ = "roc_auc_metric"
      target = metrics.MetricFrame(
          metrics=ras,
          y_true=y_true,
          y_pred=y_pred,
          sensitive_features=s_f,
      )
  
      overall = ras(y_true, y_pred)
      assert target.overall == overall
  
      for g in np.unique(s_f):
          mask = s_f == g
          expected = ras(y_true[mask], y_pred[mask])
          actual = target.by_group[g]
          assert expected == actual
  
imports: import functools
  import numpy
  import sklearn.metrics
  import fairlearn.metrics
label_desc: Use image node_id 1 for calling a global function with example usage: test_roc_auc() without return types