code: def test_f_oneway_vs_scipy_stats():
      # Test that our f_oneway gives the same result as scipy.stats
      rng = np.random.RandomState(0)
      X1 = rng.randn(10, 3)
      X2 = 1 + rng.randn(10, 3)
      f, pv = stats.f_oneway(X1, X2)
      f2, pv2 = f_oneway(X1, X2)
      assert np.allclose(f, f2)
      assert np.allclose(pv, pv2)
  
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 1 for calling a global function with example usage: test_f_oneway_vs_scipy_stats() without return types