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.
class_docstr_tok: ["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","."]
docstr: int: Number of input feature maps.
docstr_tok: ["int",":","Number","of","input","feature","maps","."]
code: def num_inputs(self) -> int:
      """int: Number of input feature maps."""
      return len(self.featmap_strides)
  
file_path: mmdetection/mmdet/models/roi_heads/roi_extractors/base_roi_extractor.py
imports: from abc import ABCMeta, abstractmethod
  from typing import List, Optional, Tuple
  import torch
  import torch.nn
  from mmcv import ops
  from mmengine.model import BaseModule
  from torch import Tensor
  from mmdet.utils import ConfigType, OptMultiConfig
label_desc: Use image node_id 2 for calling the BaseRoIExtractor obj's underlying member method code with example usage: obj.num_inputs() and returns: len