code: def test_tied_scores():
      # Test for stable sorting in k-best with tied scores.
      X_train = np.array([[0, 0, 0], [1, 1, 1]])
      y_train = [0, 1]
  
      for n_features in [1, 2, 3]:
          sel = SelectKBest(chi2, k=n_features).fit(X_train, y_train)
          X_test = sel.transform([[0, 1, 2]])
          assert_array_equal(X_test[0], np.arange(3)[-n_features:])
  
imports: import itertools
  import warnings
  import numpy
  import pytest
  from numpy.testing import assert_allclose
  from scipy import sparse, stats
  from sklearn.datasets import load_iris, make_classification, make_regression
  from sklearn.feature_selection import GenericUnivariateSelect, SelectFdr, SelectFpr, SelectFwe, SelectKBest, SelectPercentile, chi2, f_classif, f_oneway, f_regression, mutual_info_classif, mutual_info_regression, r_regression
  from sklearn.utils import safe_mask
  from sklearn.utils._testing import _convert_container, assert_almost_equal, assert_array_almost_equal, assert_array_equal, ignore_warnings
  from sklearn.utils.fixes import CSR_CONTAINERS
label_desc: Use image node_id 29 for calling a global function with example usage: test_tied_scores() without return types