code: def test_predict_shape():
      shapes = (15, 16, 17, 18)
      for n_dim in range(len(shapes)):
          shape = shapes[: n_dim + 1]
          arr = np.arange(np.prod(shape)).reshape(shape)
          slicers_list = []
          for i in range(n_dim):
              slicers_list.append(_slices_for_len(shape[i]))
              for sliceobj in product(*slicers_list):
                  assert (
                      predict_shape(sliceobj, shape)
                      == arr[sliceobj].shape
                  )
      # Try some Nones and ellipses
      assert predict_shape((Ellipsis,), (2, 3)) == (2, 3)
      assert predict_shape((Ellipsis, 1), (2, 3)) == (2,)
      assert predict_shape((1, Ellipsis), (2, 3)) == (3,)
      assert predict_shape((1, slice(None), Ellipsis), (2, 3)) == (3,)
      assert predict_shape((None,), (2, 3)) == (1, 2, 3)
      assert predict_shape((None, 1), (2, 3)) == (1, 3)
      assert predict_shape((1, None, slice(None)), (2, 3)) == (1, 3)
      assert predict_shape((1, slice(None), None), (2, 3)) == (3, 1)
  
imports: import time
  from functools import partial
  from io import BytesIO
  from itertools import product
  from threading import Lock, Thread
  import numpy
  import pytest
  from numpy.testing import assert_array_equal
  from ..fileslice import _positive_slice, _simple_fileslice, calc_slicedefs, canonical_slicers, fileslice, fill_slicer, is_fancy, optimize_read_slicers, optimize_slicer, predict_shape, read_segments, slice2len, slice2outax, slicers2segments, strided_scalar, threshold_heuristic
label_desc: Use image node_id 17 for calling a global function with example usage: test_predict_shape() without return types