class_docstr: TOOD: Task-aligned One-stage Object Detection, see https://arxiv.org/abs/2108.07755
  Args:
      backbone (nn.Layer): backbone instance
      neck (nn.Layer): 'FPN' instance
      head (nn.Layer): 'TOODHead' instance
code: def _forward(self):
      body_feats = self.backbone(self.inputs)
      fpn_feats = self.neck(body_feats)
      head_outs = self.head(fpn_feats)
      if not self.training:
          bboxes, bbox_num = self.head.post_process(
              head_outs,
              self.inputs["im_shape"],
              self.inputs["scale_factor"],
          )
          return bboxes, bbox_num
      else:
          loss = self.head.get_loss(head_outs, self.inputs)
          return loss
  
file_path: PaddleDetection/ppdet/modeling/architectures/tood.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from ppdet.core.workspace import register, create
  from .meta_arch import BaseArch
label_desc: Use image node_id 3 for calling the TOOD obj's underlying member method code with example usage: obj._forward() and returns: bboxes,  bbox_num, loss