class_docstr: Base tracker model.
  
  Args:
      momentums (dict[str:float], optional): Momentums to update the buffers.
          The `str` indicates the name of the buffer while the `float`
          indicates the momentum. Defaults to None.
      num_frames_retain (int, optional). If a track is disappeared more than
          `num_frames_retain` frames, it will be deleted in the memo.
           Defaults to 10.
class_docstr_tok: ["Base","tracker","model",".","Args",":","momentums","(","dict","[","str",":","float","]",",","optional",")",":","Momentums","to","update","the","buffers",".","The","`","str","`","indicates","the","name","of","the","buffer","while","the","`","float","`","indicates","the","momentum",".","Defaults","to","None",".","num_frames_retain","(","int",",","optional",")",".","If","a","track","is","disappeared","more","than","`","num_frames_retain","`","frames",",","it","will","be","deleted","in","the","memo",".","Defaults","to","10","."]
docstr: Get the buffer of a specific item.
  
  Args:
      item (str): The demanded item.
      ids (list[int], optional): The demanded ids. Defaults to None.
      num_samples (int, optional): Number of samples to calculate the
          results. Defaults to None.
      behavior (str, optional): Behavior to calculate the results.
          Options are `mean` | None. Defaults to None.
  
  Returns:
      Tensor: The results of the demanded item.
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code: def get(
      self,
      item: str,
      ids: Optional[list] = None,
      num_samples: Optional[int] = None,
      behavior: Optional[str] = None,
  ) -> torch.Tensor:
      """Get the buffer of a specific item.
  
      Args:
          item (str): The demanded item.
          ids (list[int], optional): The demanded ids. Defaults to None.
          num_samples (int, optional): Number of samples to calculate the
              results. Defaults to None.
          behavior (str, optional): Behavior to calculate the results.
              Options are `mean` | None. Defaults to None.
  
      Returns:
          Tensor: The results of the demanded item.
      """
      if ids is None:
          ids = self.ids
  
      outs = []
      for id in ids:
          out = self.tracks[id][item]
          if isinstance(out, list):
              if num_samples is not None:
                  out = out[-num_samples:]
                  out = torch.cat(out, dim=0)
                  if behavior == "mean":
                      out = out.mean(dim=0, keepdim=True)
                  elif behavior is None:
                      out = out[None]
                  else:
                      raise NotImplementedError()
              else:
                  out = out[-1]
          outs.append(out)
      return torch.cat(outs, dim=0)
  
file_path: mmdetection/mmdet/models/trackers/base_tracker.py
imports: from abc import ABCMeta, abstractmethod
  from typing import List, Optional, Tuple
  import torch
  import torch.nn.functional
  from addict import Dict
label_desc: Use image node_id 11 for calling the BaseTracker obj's underlying member method code with example usage: obj.get(item, ids, num_samples, behavior) and returns: torch