class_docstr: Base class for RoI extractor.
  
  Args:
      roi_layer (:obj:`ConfigDict` or dict): Specify RoI layer type and
          arguments.
      out_channels (int): Output channels of RoI layers.
      featmap_strides (list[int]): Strides of input feature maps.
      init_cfg (:obj:`ConfigDict` or dict or list[:obj:`ConfigDict` or             dict], optional): Initialization config dict. Defaults to None.
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docstr: Extractor ROI feats.
  
  Args:
      feats (Tuple[Tensor]): Multi-scale features.
      data_samples (List[TextSpottingDataSample]):
  
        - proposals(InstanceData): The proposals of text detection.
  
  Returns:
      Tensor: RoI feature.
docstr_tok: ["Extractor","ROI","feats",".","Args",":","feats","(","Tuple","[","Tensor","]",")",":","Multi-scale","features",".","data_samples","(","List","[","TextSpottingDataSample","]",")",":","-","proposals","(","InstanceData",")",":","The","proposals","of","text","detection",".","Returns",":","Tensor",":","RoI","feature","."]
code: def forward(self, feats: Tuple[Tensor], data_samples) -> Tensor:
      """Extractor ROI feats.
  
      Args:
          feats (Tuple[Tensor]): Multi-scale features.
          data_samples (List[TextSpottingDataSample]):
  
            - proposals(InstanceData): The proposals of text detection.
  
      Returns:
          Tensor: RoI feature.
      """
      pass
  
file_path: mmocr/projects/ABCNet/abcnet/model/base_roi_extractor.py
imports: from abc import ABCMeta, abstractmethod
  from typing import List, Tuple
  import torch.nn
  from mmcv import ops
  from mmengine.model import BaseModule
  from torch import Tensor
  from mmocr.utils import ConfigType, OptMultiConfig
label_desc: Use image node_id 4 for calling the BaseRoIExtractor obj's underlying member method code with example usage: obj.forward(feats, data_samples) without return types