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","."]
docstr: Network forward process.
  
  Usually includes backbone, neck and head forward without any post-
  processing.
docstr_tok: ["Network","forward","process",".","Usually","includes","backbone",",","neck","and","head","forward","without","any","post-","processing","."]
code: def _forward(
      self,
      inputs: torch.Tensor,
      data_samples: OptRecSampleList = None,
      **kwargs,
  ):
      """Network forward process.
  
      Usually includes backbone, neck and head forward without any post-
      processing.
      """
      pass
  
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 10 for calling the BaseRecognizer obj's underlying member method code with example usage: obj._forward(inputs, data_samples) without return types