code: def forward(self, feats, targets=None):
      assert len(feats) == len(
          self.fpn_strides
      ), "The size of feats is not equal to size of fpn_strides"
  
      feat_sizes = [[f.shape[-2], f.shape[-1]] for f in feats]
      cls_score_list, reg_pred_list = [], []
      obj_score_list = []
      for i, feat in enumerate(feats):
          feat = self.stem_conv[i](feat)
          cls_logit = self.conv_cls[i](feat)
          reg_pred = self.conv_reg[i](feat)
          # cls prediction
          cls_score = F.sigmoid(cls_logit)
          cls_score_list.append(
              cls_score.flatten(2).transpose([0, 2, 1])
          )
          # reg prediction
          reg_xywh, obj_logit = paddle.split(reg_pred, [4, 1], axis=1)
          reg_xywh = reg_xywh.flatten(2).transpose([0, 2, 1])
          reg_pred_list.append(reg_xywh)
          # obj prediction
          obj_score = F.sigmoid(obj_logit)
          obj_score_list.append(
              obj_score.flatten(2).transpose([0, 2, 1])
          )
  
      cls_score_list = paddle.concat(cls_score_list, axis=1)
      reg_pred_list = paddle.concat(reg_pred_list, axis=1)
      obj_score_list = paddle.concat(obj_score_list, axis=1)
  
      # bbox decode
      anchor_points, stride_tensor, _ = self._generate_anchor_point(
          feat_sizes, self.fpn_strides
      )
      reg_xy, reg_wh = paddle.split(reg_pred_list, 2, axis=-1)
      reg_xy += anchor_points / stride_tensor
      reg_wh = paddle.exp(reg_wh) * 0.5
      bbox_pred_list = paddle.concat(
          [reg_xy - reg_wh, reg_xy + reg_wh], axis=-1
      )
  
      if self.training:
          (
              anchor_points,
              stride_tensor,
              num_anchors_list,
          ) = self._generate_anchor_point(
              feat_sizes, self.fpn_strides, 0.5
          )
          yolox_losses = self.get_loss(
              [
                  cls_score_list,
                  bbox_pred_list,
                  obj_score_list,
                  anchor_points,
                  stride_tensor,
                  num_anchors_list,
              ],
              targets,
          )
          return yolox_losses
      else:
          pred_scores = (cls_score_list * obj_score_list).sqrt()
          return pred_scores, bbox_pred_list, stride_tensor
  
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 5 for calling the YOLOXHead obj's underlying member method code with example usage: obj.forward(feats, targets) and returns: yolox_losses, pred_scores,  bbox_pred_list,  stride_tensor