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: Network forward process. Usually includes backbone, neck and head
  forward without any post-processing.
  
   Args:
      inputs (Dict[str, Tensor]): of shape (N, T, C, H, W).
      data_samples (List[:obj:`TrackDataSample`], optional): The
          Data Samples. It usually includes information such as
          `gt_instance`.
  
  Returns:
      tuple[list]: A tuple of features from ``head`` forward.
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code: def _forward(
      self,
      inputs: Dict[str, Tensor],
      data_samples: OptTrackSampleList = None,
      **kwargs,
  ):
      """Network forward process. Usually includes backbone, neck and head
      forward without any post-processing.
  
       Args:
          inputs (Dict[str, Tensor]): of shape (N, T, C, H, W).
          data_samples (List[:obj:`TrackDataSample`], optional): The
              Data Samples. It usually includes information such as
              `gt_instance`.
  
      Returns:
          tuple[list]: A tuple of features from ``head`` forward.
      """
      raise NotImplementedError(
          "_forward function (namely 'tensor' mode) is not supported now"
      )
  
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 11 for calling the BaseMOTModel obj's underlying member method code with example usage: obj._forward(inputs, data_samples) without return types