def METHOD_NAME(device, dtype):
    try:
        from mmcv.ops import RoIAlignRotated
    except ModuleNotFoundError:
        pytest.skip('RoIAlignRotated op is not successfully compiled')
    if dtype is torch.half:
        pytest.skip('grad check does not support fp16')
    for case in inputs:
        np_input = np.array(case[0])
        np_rois = np.array(case[1])
        x = torch.tensor(
            np_input, dtype=dtype, device=device, requires_grad=True)
        rois = torch.tensor(np_rois, dtype=dtype, device=device)
        froipool = RoIAlignRotated((pool_h, pool_w), spatial_scale,
                                   sampling_ratio)
        if torch.__version__ == 'parrots':
            gradcheck(
                froipool, (x, rois), no_grads=[rois], delta=1e-5, pt_atol=1e-5)
        else:
            gradcheck(froipool, (x, rois), eps=1e-5, atol=1e-5)