Arguments:
            anchors: list[BoxList]
            objectness: tensor of size N, A, H, W
            box_regression: tensor of size N, A * 4, H, W

def forward_for_single_feature_map(self, anchors, objectness, box_regression):
        """
        Arguments:
            anchors: list[BoxList]
            objectness: tensor of size N, A, H, W
            box_regression: tensor of size N, A * 4, H, W
        """
        device = objectness.device
        N, A, H, W = objectness.shape

        # put in the same format as anchors
        objectness = permute_and_flatten(objectness, N, A, 1, H, W).view(N, -1)
        objectness = objectness.sigmoid()

        box_regression = permute_and_flatten(box_regression, N, A, 4, H, W)

        num_anchors = A * H * W

        pre_nms_top_n = min(self.pre_nms_top_n, num_anchors)
        objectness, topk_idx = objectness.topk(pre_nms_top_n, dim=1, sorted=True)

        batch_idx = torch.arange(N, device=device)[:, None]
        box_regression = box_regression[batch_idx, topk_idx]

        image_shapes = [box.size for box in anchors]
        concat_anchors = torch.cat([a.bbox for a in anchors], dim=0)
        concat_anchors = concat_anchors.reshape(N, -1, 4)[batch_idx, topk_idx]

        proposals = self.box_coder.decode(
            box_regression.view(-1, 4), concat_anchors.view(-1, 4)
        )

        proposals = proposals.view(N, -1, 4)

        result = []
        for proposal, score, im_shape in zip(proposals, objectness, image_shapes):
            boxlist = BoxList(proposal, im_shape, mode="xyxy")
            boxlist.add_field("objectness", score)
            boxlist = boxlist.clip_to_image(remove_empty=False)
            boxlist = remove_small_boxes(boxlist, self.min_size)
            boxlist = boxlist_nms(
                boxlist,
                self.nms_thresh,
                max_proposals=self.post_nms_top_n,
                score_field="objectness",
            )
            result.append(boxlist)
        return result