class_docstr: The roi track head.
  
  This module is used in multi-object tracking methods, such as MaskTrack
  R-CNN.
  
  Args:
      roi_extractor (dict): Configuration of roi extractor. Defaults to None.
      embed_head (dict): Configuration of embed head. Defaults to None.
      train_cfg (dict): Configuration when training. Defaults to None.
      test_cfg (dict): Configuration when testing. Defaults to None.
      init_cfg (dict): Configuration of initialization. Defaults to None.
class_docstr_tok: ["The","roi","track","head",".","This","module","is","used","in","multi-object","tracking","methods",",","such","as","MaskTrack","R-CNN",".","Args",":","roi_extractor","(","dict",")",":","Configuration","of","roi","extractor",".","Defaults","to","None",".","embed_head","(","dict",")",":","Configuration","of","embed","head",".","Defaults","to","None",".","train_cfg","(","dict",")",":","Configuration","when","training",".","Defaults","to","None",".","test_cfg","(","dict",")",":","Configuration","when","testing",".","Defaults","to","None",".","init_cfg","(","dict",")",":","Configuration","of","initialization",".","Defaults","to","None","."]
docstr: Perform forward propagation of the tracking head and predict
  tracking results on the features of the upstream network.
  
  Args:
      roi_feats (Tensor): Feature map of current images rois.
      prev_roi_feats (Tensor): Feature map of previous images rois.
  
  Returns:
      list[Tensor]: The predicted similarity_logits of each pair of key
      image and reference image.
docstr_tok: ["Perform","forward","propagation","of","the","tracking","head","and","predict","tracking","results","on","the","features","of","the","upstream","network",".","Args",":","roi_feats","(","Tensor",")",":","Feature","map","of","current","images","rois",".","prev_roi_feats","(","Tensor",")",":","Feature","map","of","previous","images","rois",".","Returns",":","list","[","Tensor","]",":","The","predicted","similarity_logits","of","each","pair","of","key","image","and","reference","image","."]
code: def predict(
      self, roi_feats: Tensor, prev_roi_feats: Tensor
  ) -> List[Tensor]:
      """Perform forward propagation of the tracking head and predict
      tracking results on the features of the upstream network.
  
      Args:
          roi_feats (Tensor): Feature map of current images rois.
          prev_roi_feats (Tensor): Feature map of previous images rois.
  
      Returns:
          list[Tensor]: The predicted similarity_logits of each pair of key
          image and reference image.
      """
      return self.embed_head.predict(roi_feats, prev_roi_feats)[0]
  
file_path: mmdetection/mmdet/models/tracking_heads/roi_track_head.py
imports: from abc import ABCMeta
  from typing import List, Optional, Tuple
  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 RoITrackHead obj's underlying member method code with example usage: obj.predict(roi_feats, prev_roi_feats) and returns: self