code: def _get_loss_bbox(
      self, boxes, gt_bbox, match_indices, num_gts, postfix=""
  ):
      # boxes: [b, query, 4], gt_bbox: list[[n, 4]]
      name_bbox = "loss_bbox" + postfix
      name_giou = "loss_giou" + postfix
  
      loss = dict()
      if sum(len(a) for a in gt_bbox) == 0:
          loss[name_bbox] = paddle.to_tensor([0.0])
          loss[name_giou] = paddle.to_tensor([0.0])
          return loss
  
      src_bbox, target_bbox = self._get_src_target_assign(
          boxes, gt_bbox, match_indices
      )
      loss[name_bbox] = (
          self.loss_coeff["bbox"]
          * F.l1_loss(src_bbox, target_bbox, reduction="sum")
          / num_gts
      )
      loss[name_giou] = self.giou_loss(
          bbox_cxcywh_to_xyxy(src_bbox),
          bbox_cxcywh_to_xyxy(target_bbox),
      )
      loss[name_giou] = loss[name_giou].sum() / num_gts
      loss[name_giou] = self.loss_coeff["giou"] * loss[name_giou]
      return loss
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import paddle
  import paddle.nn
  import paddle.nn.functional
  from ppdet.core.workspace import register
  from .iou_loss import GIoULoss
  from ..transformers import bbox_cxcywh_to_xyxy, sigmoid_focal_loss, varifocal_loss_with_logits
  from ..bbox_utils import bbox_iou
label_desc: Use image node_id 3 for calling the DETRLoss obj's underlying member method code with example usage: obj._get_loss_bbox(boxes, gt_bbox, match_indices, num_gts, postfix) and returns: loss, loss