code: def test_f_classif_constant_feature():
      # Test that f_classif warns if a feature is constant throughout.
  
      X, y = make_classification(n_samples=10, n_features=5)
      X[:, 0] = 2.0
      with pytest.warns(UserWarning):
          f_classif(X, y)
  
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 32 for calling a global function with example usage: test_f_classif_constant_feature() without return types