code: def predict_by_feat(
      self,
      all_cls_scores,
      all_bbox_preds,
      enc_cls_scores,
      enc_bbox_preds,
      enc_outputs,
      batch_img_metas,
      rescale=True,
  ):
      cls_scores = all_cls_scores[-1]
      bbox_preds = all_bbox_preds[-1]
  
      result_list = []
      for img_id in range(len(batch_img_metas)):
          cls_score = cls_scores[img_id]
          bbox_pred = bbox_preds[img_id]
          img_meta = batch_img_metas[img_id]
          results = self._predict_by_feat_single(
              cls_score, bbox_pred, img_meta, rescale
          )
          result_list.append(results)
      return result_list
  
imports: import copy
  from typing import List
  import torch
  import torch.nn
  import torch.nn.functional
  from mmcv.cnn import Linear
  from mmcv.ops import batched_nms
  from mmengine.structures import InstanceData
  from torch import Tensor
  from mmdet.models import DINOHead
  from mmdet.models.layers import CdnQueryGenerator
  from mmdet.models.layers.transformer import inverse_sigmoid
  from mmdet.models.utils import multi_apply
  from mmdet.registry import MODELS
  from mmdet.structures import SampleList
  from mmdet.structures.bbox import bbox_cxcywh_to_xyxy, bbox_overlaps, bbox_xyxy_to_cxcywh
  from mmdet.utils import InstanceList, reduce_mean
label_desc: Use image node_id 6 for calling the CoDINOHead obj's underlying member method code with example usage: obj.predict_by_feat(all_cls_scores, all_bbox_preds, enc_cls_scores, enc_bbox_preds, enc_outputs, batch_img_metas, rescale) and returns: result_list