code: def test_mixed_indexing_execution(setup):
      rs = np.random.RandomState(0)
      raw = rs.random((11, 8, 12, 13))
      arr = tensor(raw, chunk_size=6)
  
      raw_cond = raw[0, :, 0, 0] < 0.5
      cond = tensor(raw[0, :, 0, 0], chunk_size=3) < 0.5
      arr2 = arr[10::-2, cond, None, ..., :5]
      # size_res = self.executor.execute_tensor(arr2, mock=True)
      res = arr2.execute().fetch()
  
      new_shape = list(arr2.shape)
      new_shape[1] = cond.shape[0]
      # assert sum(s[0] for s in size_res) == int(np.prod(new_shape) * arr2.dtype.itemsize)
      np.testing.assert_array_equal(
          res, raw[10::-2, raw_cond, None, ..., :5]
      )
  
      b_raw = np.random.random(8)
      raw_cond = b_raw < 0.5
      conds = [raw_cond, tensor(b_raw, chunk_size=2) < 0.5]
      for cond in conds:
          arr3 = arr[-2::-3, cond, ...]
          res = arr3.execute().fetch()
  
          np.testing.assert_array_equal(res, raw[-2::-3, raw_cond, ...])
  
      # test multiple bool index and fancy index
      cond1 = np.zeros(11, dtype=bool)
      cond1[rs.permutation(11)[:5]] = True
      cond2 = np.zeros(12, dtype=bool)
      cond2[rs.permutation(12)[:5]] = True
      f3 = np.random.randint(13, size=5)
  
      expected = raw[cond1, ..., cond2, f3]
  
      t = arr[cond1, ..., cond2, f3]
      res = t.execute().fetch()
      np.testing.assert_array_equal(res, expected)
  
      t = arr[tensor(cond1), ..., tensor(cond2), tensor(f3)]
      res = t.execute().fetch()
      np.testing.assert_array_equal(res, expected)
  
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 5 for calling a global function with example usage: test_mixed_indexing_execution(setup) without return types