docstr: Args:
      out_transformer (Tuple): (feats: [num_levels, batch_size,
                                          num_queries, hidden_dim],
                      memory: [batch_size, hidden_dim, h, w],
                      src_proj: [batch_size, h*w, hidden_dim],
                      src_mask: [batch_size, 1, 1, h, w])
      body_feats (List(Tensor)): list[[B, C, H, W]]
      inputs (dict): dict(inputs)
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code: def forward(self, out_transformer, body_feats, inputs=None):
      r"""
      Args:
          out_transformer (Tuple): (feats: [num_levels, batch_size,
                                              num_queries, hidden_dim],
                          memory: [batch_size, hidden_dim, h, w],
                          src_proj: [batch_size, h*w, hidden_dim],
                          src_mask: [batch_size, 1, 1, h, w])
          body_feats (List(Tensor)): list[[B, C, H, W]]
          inputs (dict): dict(inputs)
      """
      feats, memory, src_proj, src_mask = out_transformer
      outputs_logit = self.score_head(feats)
      outputs_bbox = F.sigmoid(self.bbox_head(feats))
      outputs_seg = None
      if self.with_mask_head:
          bbox_attention_map = self.bbox_attention(
              feats[-1], memory, src_mask
          )
          fpn_feats = [a for a in body_feats[::-1]][1:]
          outputs_seg = self.mask_head(
              src_proj, bbox_attention_map, fpn_feats
          )
          outputs_seg = outputs_seg.reshape(
              [
                  feats.shape[1],
                  feats.shape[2],
                  outputs_seg.shape[-2],
                  outputs_seg.shape[-1],
              ]
          )
  
      if self.training:
          assert inputs is not None
          assert "gt_bbox" in inputs and "gt_class" in inputs
          gt_mask = (
              self.get_gt_mask_from_polygons(
                  inputs["gt_poly"], inputs["pad_mask"]
              )
              if "gt_poly" in inputs
              else None
          )
          return self.loss(
              outputs_bbox,
              outputs_logit,
              inputs["gt_bbox"],
              inputs["gt_class"],
              masks=outputs_seg,
              gt_mask=gt_mask,
          )
      else:
          return (outputs_bbox[-1], outputs_logit[-1], outputs_seg)
  
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 5 for calling the DETRHead obj's underlying member method code with example usage: obj.forward(out_transformer, body_feats, inputs) and returns: self, outputs_bbox,  outputs_logit,  outputs_seg