code: def _forward(self):
      # Backbone
      body_feats = self.backbone(self.inputs)
  
      # Neck
      if self.neck is not None:
          body_feats = self.neck(body_feats)
  
      # Transformer
      pad_mask = self.inputs.get("pad_mask", None)
      out_transformer = self.transformer(
          body_feats, pad_mask, self.inputs
      )
  
      # DETR Head
      if self.training:
          detr_losses = self.detr_head(
              out_transformer, body_feats, self.inputs
          )
          detr_losses.update(
              {
                  "loss": paddle.add_n(
                      [
                          v
                          for k, v in detr_losses.items()
                          if "log" not in k
                      ]
                  )
              }
          )
          return detr_losses
      else:
          preds = self.detr_head(out_transformer, body_feats)
          if self.exclude_post_process:
              bbox, bbox_num, mask = preds
          else:
              bbox, bbox_num, mask = self.post_process(
                  preds,
                  self.inputs["im_shape"],
                  self.inputs["scale_factor"],
                  paddle.shape(self.inputs["image"])[2:],
              )
  
          output = {"bbox": bbox, "bbox_num": bbox_num}
          if self.with_mask:
              output["mask"] = mask
          return output
  
file_path: PaddleDetection/ppdet/modeling/architectures/detr.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import paddle
  from .meta_arch import BaseArch
  from ppdet.core.workspace import register, create
label_desc: Use image node_id 3 for calling the DETR obj's underlying member method code with example usage: obj._forward() and returns: detr_losses, output