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: Build RoI operator to extract feature from each level feature map.
  
  Args:
      layer_cfg (:obj:`ConfigDict` or dict): Dictionary to construct and
          config RoI layer operation. Options are modules under
          ``mmcv/ops`` such as ``RoIAlign``.
      featmap_strides (list[int]): The stride of input feature map w.r.t
          to the original image size, which would be used to scale RoI
          coordinate (original image coordinate system) to feature
          coordinate system.
  
  Returns:
      :obj:`nn.ModuleList`: The RoI extractor modules for each level
          feature map.
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code: def build_roi_layers(
      self, layer_cfg: ConfigType, featmap_strides: List[int]
  ) -> nn.ModuleList:
      """Build RoI operator to extract feature from each level feature map.
  
      Args:
          layer_cfg (:obj:`ConfigDict` or dict): Dictionary to construct and
              config RoI layer operation. Options are modules under
              ``mmcv/ops`` such as ``RoIAlign``.
          featmap_strides (list[int]): The stride of input feature map w.r.t
              to the original image size, which would be used to scale RoI
              coordinate (original image coordinate system) to feature
              coordinate system.
  
      Returns:
          :obj:`nn.ModuleList`: The RoI extractor modules for each level
              feature map.
      """
  
      cfg = layer_cfg.copy()
      layer_type = cfg.pop("type")
      if isinstance(layer_type, str):
          assert hasattr(ops, layer_type)
          layer_cls = getattr(ops, layer_type)
      else:
          layer_cls = layer_type
      roi_layers = nn.ModuleList(
          [
              layer_cls(spatial_scale=1 / s, **cfg)
              for s in featmap_strides
          ]
      )
      return roi_layers
  
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 3 for calling the BaseRoIExtractor obj's underlying member method code with example usage: obj.build_roi_layers(layer_cfg, featmap_strides) and returns: roi_layers