code: def predict(
      self,
      feats: List[Tensor],
      batch_data_samples: SampleList,
      rescale: bool = True,
  ) -> InstanceList:
      batch_img_metas = [
          data_samples.metainfo for data_samples in batch_data_samples
      ]
      outs = self.forward(feats, batch_img_metas)
  
      predictions = self.predict_by_feat(
          *outs, batch_img_metas=batch_img_metas, rescale=rescale
      )
  
      return predictions
  
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 5 for calling the CoDINOHead obj's underlying member method code with example usage: obj.predict(feats, batch_data_samples, rescale) and returns: predictions