code: def get_loss(self, head_outs, targets):
      anchors, cls_logits, bbox_preds = head_outs
  
      feat_size = cls_logits.shape[-2:]
      cls_logits = cls_logits.transpose([0, 2, 3, 1])
      cls_logits = cls_logits.reshape([0, -1, self.num_classes])
      bbox_preds = bbox_preds.transpose([0, 2, 3, 1])
      bbox_preds = bbox_preds.reshape([0, -1, 4])
  
      num_pos_list = []
      cls_pred_list, cls_tar_list = [], []
      reg_pred_list, reg_tar_list = [], []
      # find and gather preds and targets in each image
      for cls_logit, bbox_pred, gt_bbox, gt_class, im_shape in zip(
          cls_logits,
          bbox_preds,
          targets["gt_bbox"],
          targets["gt_class"],
          targets["im_shape"],
      ):
          if self.use_inside_anchor:
              inside_mask = find_inside_anchor(
                  feat_size,
                  self.anchor_generator.strides[0],
                  self.na,
                  im_shape.tolist(),
              )
              cls_logit = cls_logit[inside_mask]
              bbox_pred = bbox_pred[inside_mask]
              anchors = anchors[inside_mask]
  
          bbox_pred = delta2bbox_v2(
              bbox_pred,
              anchors,
              self.delta_mean,
              self.delta_std,
              ctr_clip=self.ctr_clip,
          )
          bbox_pred = bbox_pred.reshape([-1, bbox_pred.shape[-1]])
  
          # -2:ignore, -1:neg, >=0:pos
          (
              match_labels,
              pos_bbox_pred,
              pos_bbox_tar,
          ) = self.bbox_assigner(bbox_pred, anchors, gt_bbox)
          pos_mask = match_labels >= 0
          neg_mask = match_labels == -1
          chosen_mask = paddle.logical_or(pos_mask, neg_mask)
  
          gt_class = gt_class.reshape([-1])
          bg_class = paddle.to_tensor(
              [self.num_classes], dtype=gt_class.dtype
          )
          # a trick to assign num_classes to negative targets
          gt_class = paddle.concat([gt_class, bg_class], axis=-1)
          match_labels = paddle.where(
              neg_mask,
              paddle.full_like(match_labels, gt_class.size - 1),
              match_labels,
          )
          num_pos_list.append(max(1.0, pos_mask.sum().item()))
  
          cls_pred_list.append(cls_logit[chosen_mask])
          cls_tar_list.append(gt_class[match_labels[chosen_mask]])
          reg_pred_list.append(pos_bbox_pred)
          reg_tar_list.append(pos_bbox_tar)
  
      num_tot_pos = paddle.to_tensor(sum(num_pos_list))
      num_tot_pos = reduce_mean(num_tot_pos).item()
      num_tot_pos = max(1.0, num_tot_pos)
  
      cls_pred = paddle.concat(cls_pred_list)
      cls_tar = paddle.concat(cls_tar_list)
      cls_loss = (
          self.loss_class(cls_pred, cls_tar, reduction="sum")
          / num_tot_pos
      )
  
      reg_pred_list = [_ for _ in reg_pred_list if _ is not None]
      reg_tar_list = [_ for _ in reg_tar_list if _ is not None]
      if len(reg_pred_list) == 0:
          reg_loss = bbox_preds.sum() * 0.0
      else:
          reg_pred = paddle.concat(reg_pred_list)
          reg_tar = paddle.concat(reg_tar_list)
          reg_loss = (
              self.loss_bbox(reg_pred, reg_tar).sum() / num_tot_pos
          )
  
      yolof_losses = {
          "loss": cls_loss + reg_loss,
          "loss_cls": cls_loss,
          "loss_reg": reg_loss,
      }
      return yolof_losses
  
imports: import math
  import numpy
  import paddle
  import paddle.nn
  import paddle.nn.functional
  from paddle import ParamAttr
  from paddle.regularizer import L2Decay
  from paddle.nn.initializer import Normal, Constant
  from ppdet.modeling.layers import MultiClassNMS
  from ppdet.core.workspace import register
  from ppdet.modeling.bbox_utils import delta2bbox_v2
label_desc: Use image node_id 3 for calling the YOLOFHead obj's underlying member method code with example usage: obj.get_loss(head_outs, targets) and returns: yolof_losses