code: def test_scoring_proba():
      from sklearn.metrics import roc_auc_score
  
      lr = LogisticRegression(solver="liblinear", multi_class="ovr")
  
      # test predict_proba
      scores = bootstrap_point632_score(
          lr,
          X[:100],
          y[:100],
          scoring_func=roc_auc_score,
          predict_proba=True,
          random_seed=123,
      )
      roc_auc = np.mean(scores)
      assert len(scores == 200)
      assert np.round(roc_auc, 2) == 1.0, roc_auc
  
      with pytest.raises(RuntimeError):
          clf = FakeClassifier()
          scores = bootstrap_point632_score(
              clf,
              X[:100],
              y[:100],
              scoring_func=roc_auc_score,
              predict_proba=True,
              random_seed=123,
          )
  
imports: import os
  import numpy
  import pandas
  import pytest
  from sklearn.base import BaseEstimator
  from sklearn.linear_model import LogisticRegression
  from sklearn.tree import DecisionTreeClassifier
  from mlxtend.data import iris_data
  from mlxtend.evaluate import bootstrap_point632_score
  from mlxtend.utils import assert_raises
label_desc: Use image node_id 10 for calling a global function with example usage: test_scoring_proba() without return types