code: def test_k_fold_cv():
      """Test OneHotEncoder with categorical_features='auto'."""
      boston = load_boston()
      clf = make_pipeline(
          OneHotEncoder(
              categorical_features="auto",
              sparse=False,
              minimum_fraction=0.05,
          ),
          LinearRegression(),
      )
  
      cross_val_score(
          clf,
          boston.data,
          boston.target,
          cv=KFold(n_splits=10, shuffle=True),
      )
  
imports: import numpy
  import scipy.sparse
  from numpy.testing import assert_array_almost_equal
  from sklearn.datasets import load_iris, load_boston
  from sklearn.linear_model import LinearRegression
  from sklearn.pipeline import make_pipeline
  from sklearn.model_selection import cross_val_score, KFold
  from nose.tools import assert_equal
  from tpot.builtins import OneHotEncoder, auto_select_categorical_features, _transform_selected
label_desc: Use image node_id 20 for calling a global function with example usage: test_k_fold_cv() without return types