code: def forward(self, scores, targets, cot_relation):
      cls_name = "loss_bbox_cls_cot"
      loss_bbox = {}
  
      tgt_labels, tgt_bboxes, tgt_gt_inds = targets
      tgt_labels = (
          paddle.concat(tgt_labels)
          if len(tgt_labels) > 1
          else tgt_labels[0]
      )
      mask = tgt_labels < self.num_classes
      valid_inds = paddle.nonzero(tgt_labels >= 0).flatten()
      if valid_inds.shape[0] == 0:
          loss_bbox[cls_name] = paddle.zeros([1], dtype="float32")
      else:
          tgt_labels = tgt_labels.cast("int64")
          valid_cot_targets = []
          for i in range(tgt_labels.shape[0]):
              train_label = tgt_labels[i]
              if train_label < self.num_classes:
                  valid_cot_targets.append(cot_relation[train_label])
          coco_targets = paddle.to_tensor(valid_cot_targets)
          coco_targets.stop_gradient = True
          coco_loss = -coco_targets * F.log_softmax(
              scores[mask][:, :-1] * self.cot_scale
          )
          loss_bbox[cls_name] = self.cot_lambda * paddle.mean(
              paddle.sum(coco_loss, axis=-1)
          )
      return loss_bbox
  
file_path: PaddleDetection/ppdet/modeling/losses/cot_loss.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import paddle
  import paddle.nn
  import paddle.nn.functional
  import numpy
  from ppdet.core.workspace import register
label_desc: Use image node_id 2 for calling the COTLoss obj's underlying member method code with example usage: obj.forward(scores, targets, cot_relation) and returns: loss_bbox