class_docstr: Implementation of RPN head.
  
  Args:
      in_channels (int): Number of channels in the input feature map.
      num_classes (int): Number of categories excluding the background
          category. Defaults to 1.
      init_cfg (:obj:`ConfigDict` or list[:obj:`ConfigDict`] or dict or             list[dict]): Initialization config dict.
      num_convs (int): Number of convolution layers in the head.
          Defaults to 1.
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.
          """
      x = self.rpn_conv(x)
      x = F.relu(x)
      rpn_cls_score = self.rpn_cls(x)
      rpn_bbox_pred = self.rpn_reg(x)
      return rpn_cls_score, rpn_bbox_pred
  
imports: import copy
  from typing import List, Optional, Tuple
  import torch
  import torch.nn
  import torch.nn.functional
  from mmcv.cnn import ConvModule
  from mmcv.ops import batched_nms
  from mmengine.config import ConfigDict
  from mmengine.structures import InstanceData
  from torch import Tensor
  from mmdet.registry import MODELS
  from mmdet.structures.bbox import cat_boxes, empty_box_as, get_box_tensor, get_box_wh, scale_boxes
  from mmdet.utils import InstanceList, MultiConfig, OptInstanceList
  from .anchor_head import AnchorHead
label_desc: Use image node_id 3 for calling the RPNHead obj's underlying member method code with example usage: obj.forward_single(x) and returns: rpn_cls_score,  rpn_bbox_pred