def METHOD_NAME(self):
    if not torch.cuda.is_available():
        return
    from mmcv.ops import DeformRoIPoolPack
    pool_h = 2
    pool_w = 2
    spatial_scale = 1.0
    sampling_ratio = 2
    for case in inputs:
        np_input = np.array(case[0])
        np_rois = np.array(case[1])
        x = torch.tensor(
            np_input, device='cuda', dtype=torch.float, requires_grad=True)
        rois = torch.tensor(np_rois, device='cuda', dtype=torch.float)
        output_c = x.size(1)
        droipool = DeformRoIPoolPack((pool_h, pool_w),
                                     output_c,
                                     spatial_scale=spatial_scale,
                                     sampling_ratio=sampling_ratio).cuda()
        if _USING_PARROTS:
            gradcheck(droipool, (x, rois), no_grads=[rois])
        else:
            gradcheck(droipool, (x, rois), eps=1e-2, atol=1e-2)