code: def foward_unsup_train(self, data_unsup_w, data_unsup_s):
      with paddle.no_grad():
          body_feats = self.teacher.backbone(data_unsup_w)
          if self.teacher.neck is not None:
              body_feats = self.teacher.neck(
                  body_feats, is_teacher=True
              )
          out_transformer = self.teacher.transformer(
              body_feats, None, data_unsup_w, is_teacher=True
          )
          preds = self.teacher.detr_head(out_transformer, body_feats)
          bbox, bbox_num = self.teacher.post_process_semi(preds)
      self.place = body_feats[0].place
  
      proposal_bbox_list = bbox[:, -4:]
      proposal_bbox_list = proposal_bbox_list.split(
          tuple(np.array(bbox_num)), 0
      )
  
      proposal_label_list = paddle.cast(bbox[:, :1], np.float32)
      proposal_label_list = proposal_label_list.split(
          tuple(np.array(bbox_num)), 0
      )
      proposal_score_list = paddle.cast(
          bbox[:, 1 : self.num_classes + 1], np.float32
      )
      proposal_score_list = proposal_score_list.split(
          tuple(np.array(bbox_num)), 0
      )
      proposal_bbox_list = [
          paddle.to_tensor(p, place=self.place)
          for p in proposal_bbox_list
      ]
      proposal_label_list = [
          paddle.to_tensor(p, place=self.place)
          for p in proposal_label_list
      ]
      # filter invalid box roughly
      if isinstance(
          self.train_cfg["pseudo_label_initial_score_thr"], float
      ):
          thr = self.train_cfg["pseudo_label_initial_score_thr"]
      else:
          # TODO: use dynamic threshold
          raise NotImplementedError(
              "Dynamic Threshold is not implemented yet."
          )
      (
          proposal_bbox_list,
          proposal_label_list,
          proposal_score_list,
      ) = list(
          zip(
              *[
                  filter_invalid(
                      proposal[:, :4],
                      proposal_label,
                      proposal_score,
                      thr=thr,
                      min_size=self.train_cfg["min_pseduo_box_size"],
                  )
                  for proposal, proposal_label, proposal_score in zip(
                      proposal_bbox_list,
                      proposal_label_list,
                      proposal_score_list,
                  )
              ]
          )
      )
  
      teacher_bboxes = list(proposal_bbox_list)
      teacher_labels = proposal_label_list
      teacher_info = [teacher_bboxes, teacher_labels]
      student_unsup = data_unsup_s
      return self.compute_pseudo_label_loss(
          student_unsup, teacher_info, proposal_score_list
      )
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from ppdet.core.workspace import register, create, merge_config
  import paddle
  import numpy
  import paddle
  import paddle.nn.functional
  from ppdet.core.workspace import register, create
  from ppdet.utils.logger import setup_logger
  from ppdet.modeling.ssod.utils import filter_invalid
  from .multi_stream_detector import MultiSteamDetector
label_desc: Use image node_id 4 for calling the DETR_SSOD obj's underlying member method code with example usage: obj.foward_unsup_train(data_unsup_w, data_unsup_s) and returns: self