code: def get_loss(self, head_outs, targets):
      (
          pred_cls,
          pred_bboxes,
          pred_obj,
          anchor_points,
          stride_tensor,
          num_anchors_list,
      ) = head_outs
      gt_labels = targets["gt_class"]
      gt_bboxes = targets["gt_bbox"]
      pred_scores = (pred_cls * pred_obj).sqrt()
      # label assignment
      center_and_strides = paddle.concat(
          [anchor_points, stride_tensor, stride_tensor], axis=-1
      )
      pos_num_list, label_list, bbox_target_list = [], [], []
      for pred_score, pred_bbox, gt_box, gt_label in zip(
          pred_scores.detach(),
          pred_bboxes.detach() * stride_tensor,
          gt_bboxes,
          gt_labels,
      ):
          pos_num, label, _, bbox_target = self.assigner(
              pred_score,
              center_and_strides,
              pred_bbox,
              gt_box,
              gt_label,
          )
          pos_num_list.append(pos_num)
          label_list.append(label)
          bbox_target_list.append(bbox_target)
      labels = paddle.to_tensor(np.stack(label_list, axis=0))
      bbox_targets = paddle.to_tensor(
          np.stack(bbox_target_list, axis=0)
      )
      bbox_targets /= stride_tensor  # rescale bbox
  
      # 1. obj score loss
      mask_positive = labels != self.num_classes
      loss_obj = F.binary_cross_entropy(
          pred_obj,
          mask_positive.astype(pred_obj.dtype).unsqueeze(-1),
          reduction="sum",
      )
  
      num_pos = sum(pos_num_list)
  
      if num_pos > 0:
          num_pos = paddle.to_tensor(num_pos, dtype=self._dtype).clip(
              min=1
          )
          loss_obj /= num_pos
  
          # 2. iou loss
          bbox_mask = mask_positive.unsqueeze(-1).tile([1, 1, 4])
          pred_bboxes_pos = paddle.masked_select(
              pred_bboxes, bbox_mask
          ).reshape([-1, 4])
          assigned_bboxes_pos = paddle.masked_select(
              bbox_targets, bbox_mask
          ).reshape([-1, 4])
          bbox_iou = bbox_overlaps(pred_bboxes_pos, assigned_bboxes_pos)
          bbox_iou = paddle.diag(bbox_iou)
  
          loss_iou = self.iou_loss(
              pred_bboxes_pos.split(4, axis=-1),
              assigned_bboxes_pos.split(4, axis=-1),
          )
          loss_iou = loss_iou.sum() / num_pos
  
          # 3. cls loss
          cls_mask = mask_positive.unsqueeze(-1).tile(
              [1, 1, self.num_classes]
          )
          pred_cls_pos = paddle.masked_select(
              pred_cls, cls_mask
          ).reshape([-1, self.num_classes])
          assigned_cls_pos = paddle.masked_select(labels, mask_positive)
          assigned_cls_pos = F.one_hot(
              assigned_cls_pos, self.num_classes + 1
          )[..., :-1]
          assigned_cls_pos *= bbox_iou.unsqueeze(-1)
          loss_cls = F.binary_cross_entropy(
              pred_cls_pos, assigned_cls_pos, reduction="sum"
          )
          loss_cls /= num_pos
  
          # 4. l1 loss
          if targets["epoch_id"] >= self.l1_epoch:
              loss_l1 = F.l1_loss(
                  pred_bboxes_pos, assigned_bboxes_pos, reduction="sum"
              )
              loss_l1 /= num_pos
          else:
              loss_l1 = paddle.zeros([1])
              loss_l1.stop_gradient = False
      else:
          loss_cls = paddle.zeros([1])
          loss_iou = paddle.zeros([1])
          loss_l1 = paddle.zeros([1])
          loss_cls.stop_gradient = False
          loss_iou.stop_gradient = False
          loss_l1.stop_gradient = False
  
      loss = (
          self.loss_weight["obj"] * loss_obj
          + self.loss_weight["cls"] * loss_cls
          + self.loss_weight["iou"] * loss_iou
      )
  
      if targets["epoch_id"] >= self.l1_epoch:
          loss += self.loss_weight["l1"] * loss_l1
  
      yolox_losses = {
          "loss": loss,
          "loss_cls": loss_cls,
          "loss_obj": loss_obj,
          "loss_iou": loss_iou,
          "loss_l1": loss_l1,
      }
      return yolox_losses
  
imports: import paddle
  import paddle.nn
  import paddle.nn.functional
  from paddle import ParamAttr
  from paddle.regularizer import L2Decay
  from ppdet.core.workspace import register
  import math
  import numpy
  from ..initializer import bias_init_with_prob, constant_
  from ..backbones.csp_darknet import BaseConv, DWConv
  from ..losses import IouLoss
  from ppdet.modeling.assigners.simota_assigner import SimOTAAssigner
  from ppdet.modeling.bbox_utils import bbox_overlaps
  from ppdet.modeling.layers import MultiClassNMS
label_desc: Use image node_id 6 for calling the YOLOXHead obj's underlying member method code with example usage: obj.get_loss(head_outs, targets) and returns: yolox_losses