def METHOD_NAME(test_case, placement, sbp):
    dims = [random(1, 2) * 8 for _ in range(2)]
    input = random_tensor(2, *dims)
    x = input.to_global(placement=placement, sbp=sbp)
    slice_tup_list = [[None, None, None], [0, 5, 2]]
    of_out = flow.slice(x.oneflow, slice_tup_list=slice_tup_list)
    torch_out = x.pytorch[:, 0:5:2]
    _check_forward_and_backward(test_case, input, of_out, torch_out)