class_docstr: The quasi-dense track head.
docstr: Perform forward propagation of the tracking head and predict
  tracking results on the features of the upstream network.
  
  Args:
      feats (list[Tensor]): Multi level feature maps of `img`.
      rescaled_bboxes (list[Tensor]): list of rescaled bboxes in sampling
          result.
  
  Returns:
      Tensor: The extracted track features.
docstr_tok: ["Perform","forward","propagation","of","the","tracking","head","and","predict","tracking","results","on","the","features","of","the","upstream","network",".","Args",":","feats","(","list","[","Tensor","]",")",":","Multi","level","feature","maps","of","`","img","`",".","rescaled_bboxes","(","list","[","Tensor","]",")",":","list","of","rescaled","bboxes","in","sampling","result",".","Returns",":","Tensor",":","The","extracted","track","features","."]
code: def predict(
      self, feats: List[Tensor], rescaled_bboxes: List[Tensor]
  ) -> Tensor:
      """Perform forward propagation of the tracking head and predict
      tracking results on the features of the upstream network.
  
      Args:
          feats (list[Tensor]): Multi level feature maps of `img`.
          rescaled_bboxes (list[Tensor]): list of rescaled bboxes in sampling
              result.
  
      Returns:
          Tensor: The extracted track features.
      """
      bbox_feats = self.extract_roi_feats(feats, rescaled_bboxes)
      track_feats = self.embed_head.predict(bbox_feats)
      return track_feats
  
file_path: mmdetection/mmdet/models/tracking_heads/quasi_dense_track_head.py
imports: from typing import List, Optional
  from mmengine.model import BaseModule
  from torch import Tensor
  from mmdet.registry import MODELS, TASK_UTILS
  from mmdet.structures import TrackSampleList
  from mmdet.structures.bbox import bbox2roi
  from mmdet.utils import InstanceList
label_desc: Use image node_id 7 for calling the QuasiDenseTrackHead obj's underlying member method code with example usage: obj.predict(feats, rescaled_bboxes) and returns: track_feats