code: def predict_roi_head(
      self,
      mlvl_feats: Tuple[Tensor],
      batch_data_samples: SampleList,
      rescale: bool = True,
  ) -> InstanceList:
      assert self.with_bbox, "Bbox head must be implemented."
      if self.with_query_head:
          batch_img_metas = [
              data_samples.metainfo
              for data_samples in batch_data_samples
          ]
          results = self.query_head.forward(mlvl_feats, batch_img_metas)
          mlvl_feats = results[-1]
      rpn_results_list = self.rpn_head.predict(
          mlvl_feats, batch_data_samples, rescale=False
      )
      return self.roi_head[self.eval_index].predict(
          mlvl_feats,
          rpn_results_list,
          batch_data_samples,
          rescale=rescale,
      )
  
imports: import copy
  from typing import Tuple, Union
  import torch
  import torch.nn
  from torch import Tensor
  from mmdet.models.detectors.base import BaseDetector
  from mmdet.registry import MODELS
  from mmdet.structures import OptSampleList, SampleList
  from mmdet.utils import InstanceList, OptConfigType, OptMultiConfig
label_desc: Use image node_id 12 for calling the CoDETR obj's underlying member method code with example usage: obj.predict_roi_head(mlvl_feats, batch_data_samples, rescale) and returns: self