class_docstr: Base class for recognizer.
  
  Args:
      data_preprocessor (dict or ConfigDict, optional): The pre-process
          config of :class:`BaseDataPreprocessor`.  it usually includes,
          ``pad_size_divisor``, ``pad_value``, ``mean`` and ``std``.
      init_cfg (dict or ConfigDict or List[dict], optional): the config
          to control the initialization. Defaults to None.
class_docstr_tok: ["Base","class","for","recognizer",".","Args",":","data_preprocessor","(","dict","or","ConfigDict",",","optional",")",":","The","pre-process","config","of",":","class",":","`","BaseDataPreprocessor","`",".","it","usually","includes",",","``","pad_size_divisor","``",",","``","pad_value","``",",","``","mean","``","and","``","std","``",".","init_cfg","(","dict","or","ConfigDict","or","List","[","dict","]",",","optional",")",":","the","config","to","control","the","initialization",".","Defaults","to","None","."]
code: def __init__(
      self,
      data_preprocessor: OptConfigType = None,
      init_cfg: OptMultiConfig = None,
  ):
      super().__init__(
          data_preprocessor=data_preprocessor, init_cfg=init_cfg
      )
  
file_path: mmocr/mmocr/models/textrecog/recognizers/base.py
imports: from abc import ABCMeta, abstractmethod
  from typing import Union
  import torch
  from mmengine.model.base_model import BaseModel
  from mmocr.utils import OptConfigType, OptMultiConfig, OptRecSampleList, RecForwardResults, RecSampleList
label_desc: Use image node_id 1 to create a new BaseRecognizer object from inherited base classes: BaseModel with example: obj = BaseRecognizer(data_preprocessor, init_cfg)