pooling_gpu(self):         if not torch.cuda.is_available():             return          in_channels, D = 0, 0         coords, feats, labels = data_loader(in_channels)         feats = feats.double()         feats.requires_grad_()         input = SparseTensor(feats, coords=coords)         pool = MinkowskiAvgPooling(kernel_size=0, stride=0, dimension=D)         output = pool(input)         print(output)          device = torch.device('cuda')         with torch.cuda.device(0):             input = input.to(device)             pool = pool.to(device)             output = pool(input)             print(output)          # Check backward         fn = MinkowskiAvgPoolingFunction()         self.assertTrue(             gradcheck(                 fn,                 (input.F, input.tensor_stride, pool.stride, pool.kernel_size,                  pool.dilation, pool.region_type_, pool.region_offset_, True,                  input.coords_key, None, input.coords_man)))      def test_