code: def test_scoring():
      from sklearn.metrics import f1_score
  
      lr = LogisticRegression(solver="liblinear", multi_class="ovr")
      scores = bootstrap_point632_score(
          lr, X[:100], y[:100], scoring_func=f1_score, random_seed=123
      )
      f1 = np.mean(scores)
      assert len(scores == 200)
      assert np.round(f1, 2) == 1.0, f1
  
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 9 for calling a global function with example usage: test_scoring() without return types