def test_DownsampleFactorMax_grad_st_extra(self):
        rng = numpy.random.RandomState(utt.fetch_seed())
        maxpoolshps = ((5, 3), (5, 3), (5, 3), (5, 5), (3, 2), (7, 7), (9, 9))
        stridesizes = ((3, 2), (7, 5), (10, 6), (1, 1),
                       (2, 3), (10, 10), (1, 1))
        imvsizs = ((16, 16), (16, 16), (16, 16), (8, 5),
                   (8, 5), (8, 5), (8, 5))

        for mode in ['max', 'sum', 'average_inc_pad', 'average_exc_pad']:
            for indx in numpy.arange(len(maxpoolshps)):
                imvsize = imvsizs[indx]
                imval = rng.rand(1, 2, imvsize[0], imvsize[1])
                stride = stridesizes[indx]
                maxpoolshp = maxpoolshps[indx]
                for ignore_border in [True, False]:
                    def mp(input):
                        return Pool(maxpoolshp,
                                    ignore_border=ignore_border,
                                    st=stride,
                                    mode=mode)(input)
                    utt.verify_grad(mp, [imval], rng=rng)
checks the gradient for the case
dataset/ETHPy150Open rizar/attention-lvcsr/libs/Theano/theano/tensor/signal/tests/test_pool.py/TestDownsampleFactorMax.test_DownsampleFactorMax_grad_st_extra