code: def get_loss(self, head_outs, gt_meta, aux_pred=None):
      (
          pred_scores,
          pred_distri,
          anchors,
          anchor_points,
          num_anchors_list,
          stride_tensor,
      ) = head_outs
  
      anchor_points_s = anchor_points / stride_tensor
      pred_bboxes = self._bbox_decode(anchor_points_s, pred_distri)
  
      if aux_pred is not None:
          pred_scores_aux = aux_pred[0]
          pred_bboxes_aux = self._bbox_decode(
              anchor_points_s, aux_pred[1]
          )
  
      gt_labels = gt_meta["gt_class"]
      gt_bboxes = gt_meta["gt_bbox"]
      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(
              anchors,
              num_anchors_list,
              gt_labels,
              gt_bboxes,
              pad_gt_mask,
              bg_index=self.num_classes,
              pred_bboxes=pred_bboxes.detach() * stride_tensor,
          )
          alpha_l = 0.25
      else:
          if self.sm_use:
              # only used in smalldet of PPYOLOE-SOD model
              (
                  assigned_labels,
                  assigned_bboxes,
                  assigned_scores,
              ) = self.assigner(
                  pred_scores.detach(),
                  pred_bboxes.detach() * stride_tensor,
                  anchor_points,
                  stride_tensor,
                  gt_labels,
                  gt_bboxes,
                  pad_gt_mask,
                  bg_index=self.num_classes,
              )
          else:
              if aux_pred is None:
                  if not hasattr(self, "assigned_labels"):
                      (
                          assigned_labels,
                          assigned_bboxes,
                          assigned_scores,
                      ) = self.assigner(
                          pred_scores.detach(),
                          pred_bboxes.detach() * stride_tensor,
                          anchor_points,
                          num_anchors_list,
                          gt_labels,
                          gt_bboxes,
                          pad_gt_mask,
                          bg_index=self.num_classes,
                      )
                      if self.for_distill:
                          self.assigned_labels = assigned_labels
                          self.assigned_bboxes = assigned_bboxes
                          self.assigned_scores = assigned_scores
  
                  else:
                      # only used in distill
                      assigned_labels = self.assigned_labels
                      assigned_bboxes = self.assigned_bboxes
                      assigned_scores = self.assigned_scores
  
              else:
                  (
                      assigned_labels,
                      assigned_bboxes,
                      assigned_scores,
                  ) = self.assigner(
                      pred_scores_aux.detach(),
                      pred_bboxes_aux.detach() * stride_tensor,
                      anchor_points,
                      num_anchors_list,
                      gt_labels,
                      gt_bboxes,
                      pad_gt_mask,
                      bg_index=self.num_classes,
                  )
          alpha_l = -1
      # rescale bbox
      assigned_bboxes /= stride_tensor
  
      assign_out_dict = self.get_loss_from_assign(
          pred_scores,
          pred_distri,
          pred_bboxes,
          anchor_points_s,
          assigned_labels,
          assigned_bboxes,
          assigned_scores,
          alpha_l,
      )
  
      if aux_pred is not None:
          assign_out_dict_aux = self.get_loss_from_assign(
              aux_pred[0],
              aux_pred[1],
              pred_bboxes_aux,
              anchor_points_s,
              assigned_labels,
              assigned_bboxes,
              assigned_scores,
              alpha_l,
          )
          loss = {}
          for key in assign_out_dict.keys():
              loss[key] = (
                  assign_out_dict[key] + assign_out_dict_aux[key]
              )
      else:
          loss = assign_out_dict
  
      return loss
  
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 15 for calling the PPYOLOEHead obj's underlying member method code with example usage: obj.get_loss(head_outs, gt_meta, aux_pred) and returns: loss