code: def test_kfold_no_shuffle(setup):
      # Manually check that KFold preserves the data ordering on toy datasets
      X2 = [[1, 2], [3, 4], [5, 6], [7, 8], [9, 10]]
  
      splits = KFold(2).split(X2[:-1])
      train, test = next(splits)
      np.testing.assert_array_equal(test.execute().fetch(), [0, 1])
      np.testing.assert_array_equal(train.execute().fetch(), [2, 3])
  
      train, test = next(splits)
      np.testing.assert_array_equal(test.execute().fetch(), [2, 3])
      np.testing.assert_array_equal(train.execute().fetch(), [0, 1])
  
      splits = KFold(2).split(X2)
      train, test = next(splits)
      np.testing.assert_array_equal(test.execute().fetch(), [0, 1, 2])
      np.testing.assert_array_equal(train.execute().fetch(), [3, 4])
  
      train, test = next(splits)
      np.testing.assert_array_equal(test.execute().fetch(), [3, 4])
      np.testing.assert_array_equal(train.execute().fetch(), [0, 1, 2])
  
imports: import itertools
  import numpy
  import pandas
  import pytest
  from ....None import dataframe
  from ....None import tensor
  from ....dataframe.core import DATAFRAME_TYPE
  from ....lib.sparse import SparseNDArray
  from ...utils.validation import _num_samples
  from ..None import train_test_split, KFold
label_desc: Use image node_id 13 for calling a global function with example usage: test_kfold_no_shuffle(setup) without return types