class_docstr: Base class for multiple object tracking.
  
  Args:
      data_preprocessor (dict or ConfigDict, optional): The pre-process
         config of :class:`TrackDataPreprocessor`.  it usually includes,
          ``pad_size_divisor``, ``pad_value``, ``mean`` and ``std``.
      init_cfg (dict or list[dict]): Initialization config dict.
class_docstr_tok: ["Base","class","for","multiple","object","tracking",".","Args",":","data_preprocessor","(","dict","or","ConfigDict",",","optional",")",":","The","pre-process","config","of",":","class",":","`","TrackDataPreprocessor","`",".","it","usually","includes",",","``","pad_size_divisor","``",",","``","pad_value","``",",","``","mean","``","and","``","std","``",".","init_cfg","(","dict","or","list","[","dict","]",")",":","Initialization","config","dict","."]
docstr: Calculate losses from a batch of inputs and data samples.
docstr_tok: ["Calculate","losses","from","a","batch","of","inputs","and","data","samples","."]
code: def loss(
      self,
      inputs: Dict[str, Tensor],
      data_samples: TrackSampleList,
      **kwargs,
  ) -> Union[dict, tuple]:
      """Calculate losses from a batch of inputs and data samples."""
      pass
  
file_path: mmdetection/mmdet/models/mot/base.py
imports: from abc import ABCMeta, abstractmethod
  from typing import Dict, List, Tuple, Union
  from mmengine.model import BaseModel
  from torch import Tensor
  from mmdet.registry import MODELS
  from mmdet.structures import OptTrackSampleList, TrackSampleList
  from mmdet.utils import OptConfigType, OptMultiConfig
label_desc: Use image node_id 9 for calling the BaseMOTModel obj's underlying member method code with example usage: obj.loss(inputs, data_samples) without return types