class_docstr: Detection Head of `FoveaBox: Beyond Anchor-based Object Detector.
  
  <https://arxiv.org/abs/1904.03797>`_.
  
  Args:
      num_classes (int): Number of categories excluding the background
          category.
      in_channels (int): Number of channels in the input feature map.
      base_edge_list (list[int]): List of edges.
      scale_ranges (list[tuple]): Range of scales.
      sigma (float): Super parameter of ``FoveaHead``.
      with_deform (bool):  Whether use deform conv.
      deform_groups (int): Deformable conv group size.
      init_cfg (:obj:`ConfigDict` or dict or list[:obj:`ConfigDict` or             dict], optional): Initialization config dict.
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docstr: Forward features of a single scale level.
  
  Args:
      x (Tensor): FPN feature maps of the specified stride.
  
  Returns:
      tuple: scores for each class and bbox predictions of input
      feature maps.
docstr_tok: ["Forward","features","of","a","single","scale","level",".","Args",":","x","(","Tensor",")",":","FPN","feature","maps","of","the","specified","stride",".","Returns",":","tuple",":","scores","for","each","class","and","bbox","predictions","of","input","feature","maps","."]
code: def forward_single(self, x: Tensor) -> Tuple[Tensor, Tensor]:
      """Forward features of a single scale level.
  
      Args:
          x (Tensor): FPN feature maps of the specified stride.
  
      Returns:
          tuple: scores for each class and bbox predictions of input
          feature maps.
      """
      cls_feat = x
      reg_feat = x
      for reg_layer in self.reg_convs:
          reg_feat = reg_layer(reg_feat)
      bbox_pred = self.conv_reg(reg_feat)
      if self.with_deform:
          cls_feat = self.feature_adaption(cls_feat, bbox_pred.exp())
      for cls_layer in self.cls_convs:
          cls_feat = cls_layer(cls_feat)
      cls_score = self.conv_cls(cls_feat)
      return cls_score, bbox_pred
  
imports: from typing import Dict, List, Optional, Tuple
  import torch
  import torch.nn
  from mmcv.cnn import ConvModule
  from mmcv.ops import DeformConv2d
  from mmengine.config import ConfigDict
  from mmengine.model import BaseModule
  from mmengine.structures import InstanceData
  from torch import Tensor
  from mmdet.registry import MODELS
  from mmdet.utils import InstanceList, OptInstanceList, OptMultiConfig
  from ..utils import filter_scores_and_topk, multi_apply
  from .anchor_free_head import AnchorFreeHead
label_desc: Use image node_id 3 for calling the FoveaHead obj's underlying member method code with example usage: obj.forward_single(x) and returns: cls_score,  bbox_pred