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 on decoded bounding boxes. Default: False
      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.
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_anchors * num_classes.
          bbox_pred (Tensor): Box energies / deltas for a single scale                     level, the channels number is num_anchors * 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_anchors","*","num_classes",".","bbox_pred","(","Tensor",")",":","Box","energies","\/","deltas","for","a","single","scale","level",",","the","channels","number","is","num_anchors","*","4","."]
code: def forward_single(self, x):
      """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_anchors * num_classes.
                  bbox_pred (Tensor): Box energies / deltas for a single scale \
                      level, the channels number is num_anchors * 4.
          """
      cls_score = self.conv_cls(x)
      bbox_pred = self.conv_reg(x)
      return cls_score, bbox_pred
  
imports: import torch
  import torch.nn
  from mmcv.cnn import normal_init
  from mmcv.runner import force_fp32
  from mmdet.core import anchor_inside_flags, build_anchor_generator, build_assigner, build_bbox_coder, build_sampler, images_to_levels, multi_apply, multiclass_nms, unmap
  from ..builder import HEADS, build_loss
  from .base_dense_head import BaseDenseHead
  from .dense_test_mixins import BBoxTestMixin
label_desc: Use image node_id 4 for calling the AnchorHead obj's underlying member method code with example usage: obj.forward_single(x) and returns: cls_score,  bbox_pred