code: def get_loss(self, head_outs, gt_meta):
      (
          pred_scores,
          pred_dist,
          pred_angle,
          anchor_points,
          num_anchors_list,
          stride_tensor,
      ) = head_outs
      # [B, N, 5] -> [B, N, 5]
      pred_bboxes = self._bbox_decode(
          anchor_points, pred_dist, pred_angle, stride_tensor
      )
      gt_labels = gt_meta["gt_class"]
      # [B, N, 5]
      gt_bboxes = gt_meta["gt_rbox"]
      pad_gt_mask = gt_meta["pad_gt_mask"]
      # label assignment
      if gt_meta["epoch_id"] < self.static_assigner_epoch:
          (
              assigned_labels,
              assigned_bboxes,
              assigned_scores,
          ) = self.static_assigner(
              anchor_points,
              stride_tensor,
              num_anchors_list,
              gt_labels,
              gt_meta["gt_bbox"],
              gt_bboxes,
              pad_gt_mask,
              self.num_classes,
              pred_bboxes.detach(),
          )
      else:
          (
              assigned_labels,
              assigned_bboxes,
              assigned_scores,
          ) = self.assigner(
              pred_scores.detach(),
              pred_bboxes.detach(),
              anchor_points,
              num_anchors_list,
              gt_labels,
              gt_bboxes,
              pad_gt_mask,
              bg_index=self.num_classes,
          )
      alpha_l = -1
      # cls loss
      if self.use_varifocal_loss:
          one_hot_label = F.one_hot(
              assigned_labels, self.num_classes + 1
          )[..., :-1]
          loss_cls = self._varifocal_loss(
              pred_scores, assigned_scores, one_hot_label
          )
      else:
          loss_cls = self._focal_loss(
              pred_scores, assigned_scores, alpha_l
          )
  
      assigned_scores_sum = assigned_scores.sum()
      if paddle.distributed.get_world_size() > 1:
          paddle.distributed.all_reduce(assigned_scores_sum)
          assigned_scores_sum = paddle.clip(
              assigned_scores_sum / paddle.distributed.get_world_size(),
              min=1.0,
          )
      else:
          assigned_scores_sum = paddle.clip(
              assigned_scores_sum, min=1.0
          )
      loss_cls /= assigned_scores_sum
  
      loss_iou, loss_dfl = self._bbox_loss(
          pred_angle,
          pred_bboxes,
          anchor_points,
          assigned_labels,
          assigned_bboxes,
          assigned_scores,
          assigned_scores_sum,
          stride_tensor,
      )
  
      loss = (
          self.loss_weight["class"] * loss_cls
          + self.loss_weight["iou"] * loss_iou
          + self.loss_weight["dfl"] * loss_dfl
      )
      out_dict = {
          "loss": loss,
          "loss_cls": loss_cls,
          "loss_iou": loss_iou,
          "loss_dfl": loss_dfl,
      }
      return out_dict
  
imports: import paddle
  import paddle.nn
  import paddle.nn.functional
  from ppdet.core.workspace import register
  from ..losses import ProbIoULoss
  from ..initializer import bias_init_with_prob, constant_, normal_, vector_
  from ppdet.modeling.backbones.cspresnet import ConvBNLayer
  from ppdet.modeling.ops import get_static_shape, get_act_fn, anchor_generator
  from ppdet.modeling.layers import MultiClassNMS
label_desc: Use image node_id 9 for calling the PPYOLOERHead obj's underlying member method code with example usage: obj.get_loss(head_outs, gt_meta) and returns: out_dict