class_docstr: Anchor-based head (RPN, RetinaNet, SSD, etc.).
  
  Args:
      num_classes (int): Number of categories excluding the background
          category.
      in_channels (int): Number of channels in the input feature map.
      feat_channels (int): Number of hidden channels. Used in child classes.
      anchor_generator (dict): Config dict for anchor generator
      bbox_coder (dict): Config of bounding box coder.
      reg_decoded_bbox (bool): If true, the regression loss would be
          applied directly on decoded bounding boxes, converting both
          the predicted boxes and regression targets to absolute
          coordinates format. Default False. It should be `True` when
          using `IoULoss`, `GIoULoss`, or `DIoULoss` in the bbox head.
      loss_cls (dict): Config of classification loss.
      loss_bbox (dict): Config of localization loss.
      train_cfg (dict): Training config of anchor head.
      test_cfg (dict): Testing config of anchor head.
      init_cfg (dict or list[dict], optional): Initialization config dict.
class_docstr_tok: ["Anchor-based","head","(","RPN",",","RetinaNet",",","SSD",",","etc.",")",".","Args",":","num_classes","(","int",")",":","Number","of","categories","excluding","the","background","category",".","in_channels","(","int",")",":","Number","of","channels","in","the","input","feature","map",".","feat_channels","(","int",")",":","Number","of","hidden","channels",".","Used","in","child","classes",".","anchor_generator","(","dict",")",":","Config","dict","for","anchor","generator","bbox_coder","(","dict",")",":","Config","of","bounding","box","coder",".","reg_decoded_bbox","(","bool",")",":","If","true",",","the","regression","loss","would","be","applied","directly","on","decoded","bounding","boxes",",","converting","both","the","predicted","boxes","and","regression","targets","to","absolute","coordinates","format",".","Default","False",".","It","should","be","`","True","`","when","using","`","IoULoss","`",",","`","GIoULoss","`",",","or","`","DIoULoss","`","in","the","bbox","head",".","loss_cls","(","dict",")",":","Config","of","classification","loss",".","loss_bbox","(","dict",")",":","Config","of","localization","loss",".","train_cfg","(","dict",")",":","Training","config","of","anchor","head",".","test_cfg","(","dict",")",":","Testing","config","of","anchor","head",".","init_cfg","(","dict","or","list","[","dict","]",",","optional",")",":","Initialization","config","dict","."]
docstr: Forward feature of a single scale level.
  
  Args:
      x (Tensor): Features of a single scale level.
  
  Returns:
      tuple:
          cls_score (Tensor): Cls scores for a single scale level                     the channels number is num_base_priors * num_classes.
          bbox_pred (Tensor): Box energies / deltas for a single scale                     level, the channels number is num_base_priors * 4.
docstr_tok: ["Forward","feature","of","a","single","scale","level",".","Args",":","x","(","Tensor",")",":","Features","of","a","single","scale","level",".","Returns",":","tuple",":","cls_score","(","Tensor",")",":","Cls","scores","for","a","single","scale","level","the","channels","number","is","num_base_priors","*","num_classes",".","bbox_pred","(","Tensor",")",":","Box","energies","\/","deltas","for","a","single","scale","level",",","the","channels","number","is","num_base_priors","*","4","."]
code: def forward_single(self, x: Tensor) -> Tuple[Tensor, Tensor]:
      """Forward feature of a single scale level.
  
          Args:
              x (Tensor): Features of a single scale level.
  
          Returns:
              tuple:
                  cls_score (Tensor): Cls scores for a single scale level \
                      the channels number is num_base_priors * num_classes.
                  bbox_pred (Tensor): Box energies / deltas for a single scale \
                      level, the channels number is num_base_priors * 4.
          """
      cls_score = self.conv_cls(x)
      bbox_pred = self.conv_reg(x)
      return cls_score, bbox_pred
  
imports: import warnings
  from typing import List, Optional, Tuple, Union
  import torch
  import torch.nn
  from mmengine.structures import InstanceData
  from torch import Tensor
  from mmdet.registry import MODELS, TASK_UTILS
  from mmdet.structures.bbox import BaseBoxes, cat_boxes, get_box_tensor
  from mmdet.utils import ConfigType, InstanceList, OptConfigType, OptInstanceList, OptMultiConfig
  from ..task_modules.prior_generators import AnchorGenerator, anchor_inside_flags
  from ..task_modules.samplers import PseudoSampler
  from ..utils import images_to_levels, multi_apply, unmap
  from .base_dense_head import BaseDenseHead
label_desc: Use image node_id 5 for calling the AnchorHead obj's underlying member method code with example usage: obj.forward_single(x) and returns: cls_score,  bbox_pred