code: def forward(self, out_transformer, body_feats, inputs=None):
      (
          dec_out_logits,
          dec_out_bboxes,
          dec_out_masks,
          enc_out,
          init_out,
          dn_meta,
      ) = out_transformer
      if self.training:
          assert inputs is not None
          assert "gt_bbox" in inputs and "gt_class" in inputs
          assert "gt_segm" in inputs
  
          if dn_meta is not None:
              dn_out_logits, dec_out_logits = paddle.split(
                  dec_out_logits, dn_meta["dn_num_split"], axis=2
              )
              dn_out_bboxes, dec_out_bboxes = paddle.split(
                  dec_out_bboxes, dn_meta["dn_num_split"], axis=2
              )
              dn_out_masks, dec_out_masks = paddle.split(
                  dec_out_masks, dn_meta["dn_num_split"], axis=2
              )
              if init_out is not None:
                  (
                      init_out_logits,
                      init_out_bboxes,
                      init_out_masks,
                  ) = init_out
                  init_out_logits_dn, init_out_logits = paddle.split(
                      init_out_logits, dn_meta["dn_num_split"], axis=1
                  )
                  init_out_bboxes_dn, init_out_bboxes = paddle.split(
                      init_out_bboxes, dn_meta["dn_num_split"], axis=1
                  )
                  init_out_masks_dn, init_out_masks = paddle.split(
                      init_out_masks, dn_meta["dn_num_split"], axis=1
                  )
  
                  dec_out_logits = paddle.concat(
                      [init_out_logits.unsqueeze(0), dec_out_logits]
                  )
                  dec_out_bboxes = paddle.concat(
                      [init_out_bboxes.unsqueeze(0), dec_out_bboxes]
                  )
                  dec_out_masks = paddle.concat(
                      [init_out_masks.unsqueeze(0), dec_out_masks]
                  )
  
                  dn_out_logits = paddle.concat(
                      [init_out_logits_dn.unsqueeze(0), dn_out_logits]
                  )
                  dn_out_bboxes = paddle.concat(
                      [init_out_bboxes_dn.unsqueeze(0), dn_out_bboxes]
                  )
                  dn_out_masks = paddle.concat(
                      [init_out_masks_dn.unsqueeze(0), dn_out_masks]
                  )
          else:
              dn_out_bboxes, dn_out_logits = None, None
              dn_out_masks = None
  
          enc_out_logits, enc_out_bboxes, enc_out_masks = enc_out
          out_logits = paddle.concat(
              [enc_out_logits.unsqueeze(0), dec_out_logits]
          )
          out_bboxes = paddle.concat(
              [enc_out_bboxes.unsqueeze(0), dec_out_bboxes]
          )
          out_masks = paddle.concat(
              [enc_out_masks.unsqueeze(0), dec_out_masks]
          )
  
          return self.loss(
              out_bboxes,
              out_logits,
              inputs["gt_bbox"],
              inputs["gt_class"],
              masks=out_masks,
              gt_mask=inputs["gt_segm"],
              dn_out_logits=dn_out_logits,
              dn_out_bboxes=dn_out_bboxes,
              dn_out_masks=dn_out_masks,
              dn_meta=dn_meta,
          )
      else:
          return (
              dec_out_bboxes[-1],
              dec_out_logits[-1],
              dec_out_masks[-1],
          )
  
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 MaskDINOHead 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,  dec_out_masks