code: def test_select_kbest_zero(dtype_in):
      # Test whether k=0 correctly returns no features.
      X, y = make_classification(
          n_samples=20, n_features=10, shuffle=False, random_state=0
      )
      X = X.astype(dtype_in)
  
      univariate_filter = SelectKBest(f_classif, k=0)
      univariate_filter.fit(X, y)
      support = univariate_filter.get_support()
      gtruth = np.zeros(10, dtype=bool)
      assert_array_equal(support, gtruth)
      with pytest.warns(UserWarning, match="No features were selected"):
          X_selected = univariate_filter.transform(X)
      assert X_selected.shape == (20, 0)
      assert X_selected.dtype == dtype_in
  
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 15 for calling a global function with example usage: test_select_kbest_zero(dtype_in) without return types