class_docstr: Adds a simple RPN Head with classification and regression heads
  
  Args:
      in_channels (int): number of channels of the input feature
      num_anchors (int): number of anchors to be predicted
      conv_depth (int, optional): number of convolutions
code: def forward(
      self, x: List[Tensor]
  ) -> Tuple[List[Tensor], List[Tensor]]:
      logits = []
      bbox_reg = []
      for feature in x:
          t = self.conv(feature)
          logits.append(self.cls_logits(t))
          bbox_reg.append(self.bbox_pred(t))
      return logits, bbox_reg
  
imports: from typing import Dict, List, Optional, Tuple
  import torch
  from torch import nn, Tensor
  from torch.nn import functional
  from torchvision.ops import boxes, Conv2dNormActivation
  from .None import _utils
  from .anchor_utils import AnchorGenerator
  from .image_list import ImageList
label_desc: Use image node_id 3 for calling the RPNHead obj's underlying member method code with example usage: obj.forward(x) and returns: logits,  bbox_reg