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 features from the upstream network.
  
  Args:
      feats (tuple[Tensor]): Features from the upstream network, each is
          a 4D-tensor.
  
  Returns:
      tuple: A tuple of classification scores and bbox prediction.
  
          - cls_scores (list[Tensor]): Classification scores for all                     scale levels, each is a 4D-tensor, the channels number                     is num_anchors * num_classes.
          - bbox_preds (list[Tensor]): Box energies / deltas for all                     scale levels, each is a 4D-tensor, the channels number                     is num_anchors * 4.
docstr_tok: ["Forward","features","from","the","upstream","network",".","Args",":","feats","(","tuple","[","Tensor","]",")",":","Features","from","the","upstream","network",",","each","is","a","4D-tensor",".","Returns",":","tuple",":","A","tuple","of","classification","scores","and","bbox","prediction",".","-","cls_scores","(","list","[","Tensor","]",")",":","Classification","scores","for","all","scale","levels",",","each","is","a","4D-tensor",",","the","channels","number","is","num_anchors","*","num_classes",".","-","bbox_preds","(","list","[","Tensor","]",")",":","Box","energies","\/","deltas","for","all","scale","levels",",","each","is","a","4D-tensor",",","the","channels","number","is","num_anchors","*","4","."]
code: def forward(self, feats):
      """Forward features from the upstream network.
  
          Args:
              feats (tuple[Tensor]): Features from the upstream network, each is
                  a 4D-tensor.
  
          Returns:
              tuple: A tuple of classification scores and bbox prediction.
  
                  - cls_scores (list[Tensor]): Classification scores for all \
                      scale levels, each is a 4D-tensor, the channels number \
                      is num_anchors * num_classes.
                  - bbox_preds (list[Tensor]): Box energies / deltas for all \
                      scale levels, each is a 4D-tensor, the channels number \
                      is num_anchors * 4.
          """
      return multi_apply(self.forward_single, feats)
  
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 5 for calling the AnchorHead obj's underlying member method code with example usage: obj.forward(feats) and returns: multi_apply