Arguments:
            anchors: list[list[BoxList]]
            objectness: list[tensor]
            box_regression: list[tensor]

        Returns:
            boxlists (list[BoxList]): the post-processed anchors, after
                applying box decoding and NMS

def forward(self, anchors, objectness, box_regression, targets=None):
        """
        Arguments:
            anchors: list[list[BoxList]]
            objectness: list[tensor]
            box_regression: list[tensor]

        Returns:
            boxlists (list[BoxList]): the post-processed anchors, after
                applying box decoding and NMS
        """
        sampled_boxes = []
        num_levels = len(objectness)
        anchors = list(zip(*anchors))
        for a, o, b in zip(anchors, objectness, box_regression):
            sampled_boxes.append(self.forward_for_single_feature_map(a, o, b))

        boxlists = list(zip(*sampled_boxes))
        boxlists = [cat_boxlist(boxlist) for boxlist in boxlists]

        if num_levels > 1:
            boxlists = self.select_over_all_levels(boxlists)

        # append ground-truth bboxes to proposals
        if self.training and targets is not None:
            boxlists = self.add_gt_proposals(boxlists, targets)

        return boxlists