code: def test_numeric_stability(i):
      X_init = np.array([2.0, 4.0, 6.0, 8.0, 10.0]).reshape(-1, 1)
      Xt_expected = np.array([0, 0, 1, 1, 1]).reshape(-1, 1)
  
      # Test up to discretizing nano units
      X = X_init / 10**i
      Xt = KBinsDiscretizer(n_bins=2, encode="ordinal").fit_transform(X)
      assert_array_equal(Xt_expected, Xt)
  
imports: import warnings
  import numpy
  import pytest
  import scipy.sparse
  from sklearn import clone
  from sklearn.preprocessing import KBinsDiscretizer, OneHotEncoder
  from sklearn.utils._testing import assert_allclose, assert_allclose_dense_sparse, assert_array_almost_equal, assert_array_equal
label_desc: Use image node_id 10 for calling a global function with example usage: test_numeric_stability(i) without return types