code: def forward(self, out_transformer, body_feats, inputs=None):
      (
          dec_out_bboxes,
          dec_out_logits,
          enc_topk_bboxes,
          enc_topk_logits,
          dn_meta,
      ) = out_transformer
      if self.training:
          assert inputs is not None
          assert "gt_bbox" in inputs and "gt_class" in inputs
  
          if dn_meta is not None:
              if isinstance(dn_meta, list):
                  dual_groups = len(dn_meta) - 1
                  dec_out_bboxes = paddle.split(
                      dec_out_bboxes, dual_groups + 1, axis=2
                  )
                  dec_out_logits = paddle.split(
                      dec_out_logits, dual_groups + 1, axis=2
                  )
                  enc_topk_bboxes = paddle.split(
                      enc_topk_bboxes, dual_groups + 1, axis=1
                  )
                  enc_topk_logits = paddle.split(
                      enc_topk_logits, dual_groups + 1, axis=1
                  )
  
                  dec_out_bboxes_list = []
                  dec_out_logits_list = []
                  dn_out_bboxes_list = []
                  dn_out_logits_list = []
                  loss = {}
                  for g_id in range(dual_groups + 1):
                      if dn_meta[g_id] is not None:
                          (
                              dn_out_bboxes_gid,
                              dec_out_bboxes_gid,
                          ) = paddle.split(
                              dec_out_bboxes[g_id],
                              dn_meta[g_id]["dn_num_split"],
                              axis=2,
                          )
                          (
                              dn_out_logits_gid,
                              dec_out_logits_gid,
                          ) = paddle.split(
                              dec_out_logits[g_id],
                              dn_meta[g_id]["dn_num_split"],
                              axis=2,
                          )
                      else:
                          dn_out_bboxes_gid, dn_out_logits_gid = (
                              None,
                              None,
                          )
                          dec_out_bboxes_gid = dec_out_bboxes[g_id]
                          dec_out_logits_gid = dec_out_logits[g_id]
                      out_bboxes_gid = paddle.concat(
                          [
                              enc_topk_bboxes[g_id].unsqueeze(0),
                              dec_out_bboxes_gid,
                          ]
                      )
                      out_logits_gid = paddle.concat(
                          [
                              enc_topk_logits[g_id].unsqueeze(0),
                              dec_out_logits_gid,
                          ]
                      )
                      loss_gid = self.loss(
                          out_bboxes_gid,
                          out_logits_gid,
                          inputs["gt_bbox"],
                          inputs["gt_class"],
                          dn_out_bboxes=dn_out_bboxes_gid,
                          dn_out_logits=dn_out_logits_gid,
                          dn_meta=dn_meta[g_id],
                      )
                      # sum loss
                      for key, value in loss_gid.items():
                          loss.update(
                              {
                                  key: loss.get(key, paddle.zeros([1]))
                                  + value
                              }
                          )
  
                  # average across (dual_groups + 1)
                  for key, value in loss.items():
                      loss.update({key: value / (dual_groups + 1)})
                  return loss
              else:
                  dn_out_bboxes, dec_out_bboxes = paddle.split(
                      dec_out_bboxes, dn_meta["dn_num_split"], axis=2
                  )
                  dn_out_logits, dec_out_logits = paddle.split(
                      dec_out_logits, dn_meta["dn_num_split"], axis=2
                  )
          else:
              dn_out_bboxes, dn_out_logits = None, None
  
          out_bboxes = paddle.concat(
              [enc_topk_bboxes.unsqueeze(0), dec_out_bboxes]
          )
          out_logits = paddle.concat(
              [enc_topk_logits.unsqueeze(0), dec_out_logits]
          )
  
          return self.loss(
              out_bboxes,
              out_logits,
              inputs["gt_bbox"],
              inputs["gt_class"],
              dn_out_bboxes=dn_out_bboxes,
              dn_out_logits=dn_out_logits,
              dn_meta=dn_meta,
              gt_score=inputs.get("gt_score", None),
          )
      else:
          return (
              dec_out_bboxes[self.eval_idx],
              dec_out_logits[self.eval_idx],
              None,
          )
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import paddle
  import paddle.nn
  import paddle.nn.functional
  from ppdet.core.workspace import register
  import pycocotools.mask
  from ..initializer import linear_init_, constant_
  from ..transformers.utils import inverse_sigmoid
label_desc: Use image node_id 2 for calling the DINOHead obj's underlying member method code with example usage: obj.forward(out_transformer, body_feats, inputs) and returns: self, dec_out_bboxes,  dec_out_logits,  None, loss