class_docstr: add a RPN head with classification and regression
  通过滑动窗口计算预测目标概率与bbox regression参数
  
  Arguments:
      in_channels: number of channels of the input feature
      num_anchors: number of anchors to be predicted
class_docstr_tok: ["add","a","RPN","head","with","classification","and","regression","\u901a\u8fc7\u6ed1\u52a8\u7a97\u53e3\u8ba1\u7b97\u9884\u6d4b\u76ee\u6807\u6982\u7387\u4e0ebbox","regression\u53c2\u6570","Arguments",":","in_channels",":","number","of","channels","of","the","input","feature","num_anchors",":","number","of","anchors","to","be","predicted"]
code: def forward(self, x):
      # type: (List[Tensor]) -> Tuple[List[Tensor], List[Tensor]]
      logits = []
      bbox_reg = []
      for i, feature in enumerate(x):
          t = F.relu(self.conv(feature))
          logits.append(self.cls_logits(t))
          bbox_reg.append(self.bbox_pred(t))
      return logits, bbox_reg
  
imports: from typing import List, Optional, Dict, Tuple
  import torch
  from torch import nn, Tensor
  from torch.nn import functional
  import torchvision
  from .None import det_utils
  from .None import boxes
  from .image_list import ImageList
label_desc: Use image node_id 2 for calling the RPNHead obj's underlying member method code with example usage: obj.forward(x) and returns: logits,  bbox_reg