code: def test_bool_indexing_execution(setup):
      raw = np.random.random((11, 8, 12, 14))
      arr = tensor(raw, chunk_size=6)
  
      index = arr < 0.5
      arr2 = arr[index]
      # size_res = self.executor.execute_tensor(arr2, mock=True)
      res = arr2.execute().fetch()
  
      # assert sum(s[0] for s in size_res) == arr.nbytes
      np.testing.assert_array_equal(
          np.sort(res), np.sort(raw[raw < 0.5])
      )
  
      index2 = tensor(raw[:, :, 0, 0], chunk_size=3) < 0.5
      arr3 = arr[index2]
      res = arr3.execute().fetch()
  
      expected = raw[raw[:, :, 0, 0] < 0.5]
      assert sum(it.size for it in res) == expected.size
      assert res.shape == expected.shape
  
      raw = np.asfortranarray(np.random.random((11, 8, 12, 14)))
      arr = tensor(raw, chunk_size=3)
  
      index = tensor(raw[:, :, 0, 0], chunk_size=3) < 0.5
      arr2 = arr[index]
      res = arr2.execute().fetch()
      expected = raw[raw[:, :, 0, 0] < 0.5].copy("A")
  
      assert res.flags["C_CONTIGUOUS"] == expected.flags["C_CONTIGUOUS"]
      assert res.flags["F_CONTIGUOUS"] == expected.flags["F_CONTIGUOUS"]
  
imports: import numpy
  import scipy.sparse
  import pytest
  from ....config import options
  from ...None import mod, stack, hstack
  from ...datasource import tensor, arange, zeros
  from ..None import take, compress, extract, choose, unravel_index, nonzero, flatnonzero, fill_diagonal
label_desc: Use image node_id 1 for calling a global function with example usage: test_bool_indexing_execution(setup) without return types