class_docstr: Extract RoI features from a single level feature map.
  
  If there are multiple input feature levels, each RoI is mapped to a level
  according to its scale. The mapping rule is proposed in
  `FPN <https://arxiv.org/abs/1612.03144>`_.
  
  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.
      finest_scale (int): Scale threshold of mapping to level 0.
          Defaults to 56.
      init_cfg (:obj:`ConfigDict` or dict or list[:obj:`ConfigDict` or             dict], optional): Initialization config dict. Defaults to None.
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code: def __init__(
      self,
      roi_layer: ConfigType,
      out_channels: int,
      featmap_strides: List[int],
      finest_scale: int = 96,
      init_cfg: OptMultiConfig = None,
  ) -> None:
      super().__init__(
          roi_layer=roi_layer,
          out_channels=out_channels,
          featmap_strides=featmap_strides,
          init_cfg=init_cfg,
      )
      self.finest_scale = finest_scale
  
file_path: mmocr/projects/ABCNet/abcnet/model/bezier_roi_extractor.py
imports: from typing import List, Tuple
  import torch
  from mmengine.structures import InstanceData
  from torch import Tensor
  from mmocr.registry import MODELS
  from mmocr.utils import ConfigType, OptMultiConfig
  from .base_roi_extractor import BaseRoIExtractor
label_desc: Use image node_id 1 to create a new BezierRoIExtractor object from inherited base classes: BaseRoIExtractor with example: obj = BezierRoIExtractor(roi_layer, out_channels, featmap_strides, finest_scale, init_cfg)