class_docstr: Build CWD distill model.                                                                                                                               
  Args:                                                                                                                                                  
      cfg: The student config.                                                                                                                           
      slim_cfg: The teacher and distill config.                                                                                                          
class_docstr_tok: ["Build","CWD","distill","model",".","Args",":","cfg",":","The","student","config",".","slim_cfg",":","The","teacher","and","distill","config","."]
code: def get_loss_gfl(self, stu_fea_list, tea_fea_list, inputs):
      loss = {}
      head_outs = self.student_model.head(stu_fea_list)
      loss_gfl = self.student_model.head.get_loss(head_outs, inputs)
      loss.update(loss_gfl)
      total_loss = paddle.add_n(list(loss.values()))
      loss.update({"loss": total_loss})
  
      feat_loss = {}
      loss_dict = {}
      s_cls_feat, t_cls_feat = [], []
      for s_neck_f, t_neck_f in zip(stu_fea_list, tea_fea_list):
          conv_cls_feat, _ = self.student_model.head.conv_feat(s_neck_f)
          cls_score = self.student_model.head.gfl_head_cls(
              conv_cls_feat
          )
          t_conv_cls_feat, _ = self.teacher_model.head.conv_feat(
              t_neck_f
          )
          t_cls_score = self.teacher_model.head.gfl_head_cls(
              t_conv_cls_feat
          )
          s_cls_feat.append(cls_score)
          t_cls_feat.append(t_cls_score)
  
      for idx, k in enumerate(self.distill_loss):
          loss_dict[k] = self.distill_loss[k](
              s_cls_feat[idx], t_cls_feat[idx]
          )
          feat_loss[f"neck_f_{idx}"] = self.distill_loss[k](
              stu_fea_list[idx], tea_fea_list[idx]
          )
  
      for k in feat_loss:
          loss["loss"] += feat_loss[k]
          loss[k] = feat_loss[k]
  
      for k in loss_dict:
          loss["loss"] += loss_dict[k]
          loss[k] = loss_dict[k]
      return loss
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import paddle
  import paddle.nn
  from ppdet.core.workspace import register, create, load_config
  from ppdet.utils.checkpoint import load_pretrain_weight
  from ppdet.utils.logger import setup_logger
label_desc: Use image node_id 4 for calling the CWDDistillModel obj's underlying member method code with example usage: obj.get_loss_gfl(stu_fea_list, tea_fea_list, inputs) and returns: loss