code: def test_all_any_execution(setup):
      raw1 = np.zeros((10, 15))
      raw2 = np.ones((10, 15))
      raw3 = np.array(
          [
              [True, False, True, False],
              [True, True, True, True],
              [False, False, False, False],
              [False, True, False, True],
          ]
      )
  
      arr1 = tensor(raw1, chunk_size=3)
      arr2 = tensor(raw2, chunk_size=3)
      arr3 = tensor(raw3, chunk_size=4)
  
      assert not arr1.all().execute().fetch()
      assert arr2.all().execute().fetch()
      assert not arr1.any().execute().fetch()
      np.testing.assert_array_equal(
          raw3.all(axis=1), arr3.all(axis=1).execute().fetch()
      )
      np.testing.assert_array_equal(
          raw3.any(axis=0), arr3.any(axis=0).execute().fetch()
      )
  
      raw = sps.random(10, 10, density=0.5) > 0.5
  
      arr = tensor(raw, chunk_size=3)
  
      assert raw.A.all() == arr.all().execute().fetch()
      assert raw.A.any() == arr.any().execute().fetch()
  
      # test string dtype
      a = tensor(list("abcdefghi"), dtype=object)
      assert a.all().execute().fetch() == "i"
      a = tensor(list("abcdefghi"), dtype=object, chunk_size=2)
      assert a.any().execute().fetch() == "a"
  
imports: import numpy
  import scipy.sparse
  import pytest
  from ....utils import ignore_warning
  from ...datasource import ones, tensor
  from ..None import mean, nansum, nanmax, nanmin, nanmean, nanprod, nanargmax, nanargmin, nanvar, nanstd, count_nonzero, allclose, array_equal, var, std, nancumsum, nancumprod
label_desc: Use image node_id 3 for calling a global function with example usage: test_all_any_execution(setup) without return types