class_docstr: RPN head.
  
  Args:
      in_channels (int): Number of channels in the input feature map.
class_docstr_tok: ["RPN","head",".","Args",":","in_channels","(","int",")",":","Number","of","channels","in","the","input","feature","map","."]
docstr: Forward feature map of a single scale level.
docstr_tok: ["Forward","feature","map","of","a","single","scale","level","."]
code: def forward_single(self, x):
      """Forward feature map of a single scale level."""
      x = self.rpn_conv(x)
      x = F.relu(x, inplace=True)
      rpn_cls_score = self.rpn_cls(x)
      rpn_bbox_pred = self.rpn_reg(x)
      return rpn_cls_score, rpn_bbox_pred
  
file_path: insightface/detection/scrfd/mmdet/models/dense_heads/rpn_head.py
imports: import torch
  import torch.nn
  import torch.nn.functional
  from mmcv.cnn import normal_init
  from mmcv.ops import batched_nms
  from ..builder import HEADS
  from .anchor_head import AnchorHead
  from .rpn_test_mixin import RPNTestMixin
label_desc: Use image node_id 4 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