docstr: Perform forward propagation and loss calculation of the bbox head on
  the features of the upstream network.
  
  Args:
      x (tuple[Tensor]): List of multi-level img features.
      sampling_results (list["obj:`SamplingResult`]): Sampling results.
  
  Returns:
      dict[str, Tensor]: Usually returns a dictionary with keys:
  
          - `cls_score` (Tensor): Classification scores.
          - `bbox_pred` (Tensor): Box energies / deltas.
          - `bbox_feats` (Tensor): Extract bbox RoI features.
          - `loss_bbox` (dict): A dictionary of bbox loss components.
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code: def bbox_loss(
      self, x: Tuple[Tensor], sampling_results: List[SamplingResult]
  ) -> dict:
      """Perform forward propagation and loss calculation of the bbox head on
      the features of the upstream network.
  
      Args:
          x (tuple[Tensor]): List of multi-level img features.
          sampling_results (list["obj:`SamplingResult`]): Sampling results.
  
      Returns:
          dict[str, Tensor]: Usually returns a dictionary with keys:
  
              - `cls_score` (Tensor): Classification scores.
              - `bbox_pred` (Tensor): Box energies / deltas.
              - `bbox_feats` (Tensor): Extract bbox RoI features.
              - `loss_bbox` (dict): A dictionary of bbox loss components.
      """
      rois = bbox2roi([res.priors for res in sampling_results])
      bbox_results = self._bbox_forward(x, rois)
  
      bbox_loss_and_target = self.bbox_head.loss_and_target(
          cls_score=bbox_results["cls_score"],
          bbox_pred=bbox_results["bbox_pred"],
          rois=rois,
          sampling_results=sampling_results,
          rcnn_train_cfg=self.train_cfg,
      )
  
      bbox_results.update(loss_bbox=bbox_loss_and_target["loss_bbox"])
      # diff
      bbox_results.update(
          bbox_targets=bbox_loss_and_target["bbox_targets"]
      )
      return bbox_results
  
file_path: mmdetection/projects/CO-DETR/codetr/co_roi_head.py
imports: from typing import List, Tuple
  import torch
  from torch import Tensor
  from mmdet.models.roi_heads import StandardRoIHead
  from mmdet.models.task_modules.samplers import SamplingResult
  from mmdet.models.utils import unpack_gt_instances
  from mmdet.registry import MODELS
  from mmdet.structures import DetDataSample
  from mmdet.structures.bbox import bbox2roi
  from mmdet.utils import InstanceList
label_desc: Use image node_id 2 for calling the CoStandardRoIHead obj's underlying member method code with example usage: obj.bbox_loss(x, sampling_results) and returns: bbox_results