class_docstr: SSD head used in https://arxiv.org/abs/1512.02325.
  
  Args:
      num_classes (int): Number of categories excluding the background
          category.
      in_channels (int): Number of channels in the input feature map.
      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
      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:
          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",":","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:
              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.
      """
      cls_scores = []
      bbox_preds = []
      for feat, reg_conv, cls_conv in zip(
          feats, self.reg_convs, self.cls_convs
      ):
          cls_scores.append(cls_conv(feat))
          bbox_preds.append(reg_conv(feat))
      return cls_scores, bbox_preds
  
file_path: insightface/detection/scrfd/mmdet/models/dense_heads/ssd_head.py
imports: import torch
  import torch.nn
  import torch.nn.functional
  from mmcv.cnn import xavier_init
  from mmcv.runner import force_fp32
  from mmdet.core import build_anchor_generator, build_assigner, build_bbox_coder, build_sampler, multi_apply
  from ..builder import HEADS
  from ..losses import smooth_l1_loss
  from .anchor_head import AnchorHead
label_desc: Use image node_id 3 for calling the SSDHead obj's underlying member method code with example usage: obj.forward(feats) and returns: cls_scores,  bbox_preds