docstr: body_feats (list[Tensor]): Feature maps from backbone
  rois (list[Tensor]): RoIs generated from RPN module
  rois_num (Tensor): The number of RoIs in each image
  inputs (dict{Tensor}): The ground-truth of image
docstr_tok: ["body_feats","(","list","[","Tensor","]",")",":","Feature","maps","from","backbone","rois","(","list","[","Tensor","]",")",":","RoIs","generated","from","RPN","module","rois_num","(","Tensor",")",":","The","number","of","RoIs","in","each","image","inputs","(","dict","{","Tensor","}",")",":","The","ground-truth","of","image"]
code: def forward(
      self,
      body_feats=None,
      rois=None,
      rois_num=None,
      inputs=None,
      cot=False,
  ):
      """
      body_feats (list[Tensor]): Feature maps from backbone
      rois (list[Tensor]): RoIs generated from RPN module
      rois_num (Tensor): The number of RoIs in each image
      inputs (dict{Tensor}): The ground-truth of image
      """
      if self.training:
          rois, rois_num, targets = self.bbox_assigner(
              rois, rois_num, inputs
          )
          self.assigned_rois = (rois, rois_num)
          self.assigned_targets = targets
  
      rois_feat = self.roi_extractor(body_feats, rois, rois_num)
      bbox_feat = self.head(rois_feat)
      if self.with_pool:
          feat = F.adaptive_avg_pool2d(bbox_feat, output_size=1)
          feat = paddle.squeeze(feat, axis=[2, 3])
      else:
          feat = bbox_feat
      if self.use_cot:
          scores = self.cot_bbox_score(feat)
          cot_scores = self.bbox_score(feat)
      else:
          scores = self.bbox_score(feat)
      deltas = self.bbox_delta(feat)
  
      if self.training:
          loss = self.get_loss(
              scores,
              deltas,
              targets,
              rois,
              self.bbox_weight,
              loss_normalize_pos=self.loss_normalize_pos,
          )
  
          if self.cot_relation is not None:
              loss_cot = self.loss_cot(
                  cot_scores, targets, self.cot_relation
              )
              loss.update(loss_cot)
          return loss, bbox_feat
      else:
          if cot:
              pred = self.get_prediction(cot_scores, deltas)
          else:
              pred = self.get_prediction(scores, deltas)
          return pred, self.head
  
imports: import numpy
  import paddle
  import paddle.nn
  import paddle.nn.functional
  from paddle.nn.initializer import Normal, XavierUniform, KaimingNormal
  from paddle.regularizer import L2Decay
  from ppdet.core.workspace import register, create
  from .roi_extractor import RoIAlign
  from ..shape_spec import ShapeSpec
  from ..bbox_utils import bbox2delta
  from ..cls_utils import _get_class_default_kwargs
  from ppdet.modeling.layers import ConvNormLayer
label_desc: Use image node_id 4 for calling the BBoxHead obj's underlying member method code with example usage: obj.forward(body_feats, rois, rois_num, inputs, cot) and returns: loss,  bbox_feat, pred,  self