code: def _bbox_loss(
      self,
      pred_dist,
      pred_bboxes,
      anchor_points,
      assigned_labels,
      assigned_bboxes,
      assigned_scores,
      assigned_scores_sum,
  ):
      # select positive samples mask
      mask_positive = assigned_labels != self.num_classes
  
      if self.for_distill:
          # only used for LD main_kd distill
          self.distill_pairs["mask_positive_select"] = mask_positive
  
      num_pos = mask_positive.sum()
      # pos/neg loss
      if num_pos > 0:
          # l1 + iou
          bbox_mask = (
              mask_positive.astype("int32")
              .unsqueeze(-1)
              .tile([1, 1, 4])
              .astype("bool")
          )
          pred_bboxes_pos = paddle.masked_select(
              pred_bboxes, bbox_mask
          ).reshape([-1, 4])
          assigned_bboxes_pos = paddle.masked_select(
              assigned_bboxes, bbox_mask
          ).reshape([-1, 4])
          bbox_weight = paddle.masked_select(
              assigned_scores.sum(-1), mask_positive
          ).unsqueeze(-1)
  
          loss_l1 = F.l1_loss(pred_bboxes_pos, assigned_bboxes_pos)
  
          loss_iou = (
              self.iou_loss(pred_bboxes_pos, assigned_bboxes_pos)
              * bbox_weight
          )
          loss_iou = loss_iou.sum() / assigned_scores_sum
  
          dist_mask = (
              mask_positive.unsqueeze(-1)
              .astype("int32")
              .tile([1, 1, self.reg_channels * 4])
              .astype("bool")
          )
          pred_dist_pos = paddle.masked_select(
              pred_dist, dist_mask
          ).reshape([-1, 4, self.reg_channels])
          assigned_ltrb = self._bbox2distance(
              anchor_points, assigned_bboxes
          )
          assigned_ltrb_pos = paddle.masked_select(
              assigned_ltrb, bbox_mask
          ).reshape([-1, 4])
          loss_dfl = (
              self._df_loss(
                  pred_dist_pos, assigned_ltrb_pos, self.reg_range[0]
              )
              * bbox_weight
          )
          loss_dfl = loss_dfl.sum() / assigned_scores_sum
          if self.for_distill:
              self.distill_pairs["pred_bboxes_pos"] = pred_bboxes_pos
              self.distill_pairs["pred_dist_pos"] = pred_dist_pos
              self.distill_pairs["bbox_weight"] = bbox_weight
      else:
          loss_l1 = paddle.zeros([1])
          loss_iou = paddle.zeros([1])
          loss_dfl = pred_dist.sum() * 0.0
      return loss_l1, loss_iou, loss_dfl
  
imports: import paddle
  import paddle.nn
  import paddle.nn.functional
  from ppdet.core.workspace import register
  from paddle import ParamAttr
  from paddle.nn.initializer import KaimingNormal
  from paddle.nn.initializer import Normal, Constant
  from ..bbox_utils import batch_distance2bbox
  from ..losses import GIoULoss
  from ..initializer import bias_init_with_prob, constant_, normal_
  from ..assigners.utils import generate_anchors_for_grid_cell
  from ppdet.modeling.backbones.cspresnet import ConvBNLayer, RepVggBlock
  from ppdet.modeling.ops import get_static_shape, get_act_fn
  from ppdet.modeling.layers import MultiClassNMS
label_desc: Use image node_id 14 for calling the PPYOLOEHead obj's underlying member method code with example usage: obj._bbox_loss(pred_dist, pred_bboxes, anchor_points, assigned_labels, assigned_bboxes, assigned_scores, assigned_scores_sum) and returns: loss_l1,  loss_iou,  loss_dfl