class_docstr: Implements Region Proposal Network (RPN).
  
  Args:
      anchor_generator (AnchorGenerator): module that generates the anchors for a set of feature
          maps.
      head (nn.Module): module that computes the objectness and regression deltas
      fg_iou_thresh (float): minimum IoU between the anchor and the GT box so that they can be
          considered as positive during training of the RPN.
      bg_iou_thresh (float): maximum IoU between the anchor and the GT box so that they can be
          considered as negative during training of the RPN.
      batch_size_per_image (int): number of anchors that are sampled during training of the RPN
          for computing the loss
      positive_fraction (float): proportion of positive anchors in a mini-batch during training
          of the RPN
      pre_nms_top_n (Dict[str, int]): number of proposals to keep before applying NMS. It should
          contain two fields: training and testing, to allow for different values depending
          on training or evaluation
      post_nms_top_n (Dict[str, int]): number of proposals to keep after applying NMS. It should
          contain two fields: training and testing, to allow for different values depending
          on training or evaluation
      nms_thresh (float): NMS threshold used for postprocessing the RPN proposals
code: def assign_targets_to_anchors(
      self, anchors: List[Tensor], targets: List[Dict[str, Tensor]]
  ) -> Tuple[List[Tensor], List[Tensor]]:
      labels = []
      matched_gt_boxes = []
      for anchors_per_image, targets_per_image in zip(anchors, targets):
          gt_boxes = targets_per_image["boxes"]
  
          if gt_boxes.numel() == 0:
              # Background image (negative example)
              device = anchors_per_image.device
              matched_gt_boxes_per_image = torch.zeros(
                  anchors_per_image.shape,
                  dtype=torch.float32,
                  device=device,
              )
              labels_per_image = torch.zeros(
                  (anchors_per_image.shape[0],),
                  dtype=torch.float32,
                  device=device,
              )
          else:
              match_quality_matrix = self.box_similarity(
                  gt_boxes, anchors_per_image
              )
              matched_idxs = self.proposal_matcher(match_quality_matrix)
              # get the targets corresponding GT for each proposal
              # NB: need to clamp the indices because we can have a single
              # GT in the image, and matched_idxs can be -2, which goes
              # out of bounds
              matched_gt_boxes_per_image = gt_boxes[
                  matched_idxs.clamp(min=0)
              ]
  
              labels_per_image = matched_idxs >= 0
              labels_per_image = labels_per_image.to(
                  dtype=torch.float32
              )
  
              # Background (negative examples)
              bg_indices = (
                  matched_idxs
                  == self.proposal_matcher.BELOW_LOW_THRESHOLD
              )
              labels_per_image[bg_indices] = 0.0
  
              # discard indices that are between thresholds
              inds_to_discard = (
                  matched_idxs
                  == self.proposal_matcher.BETWEEN_THRESHOLDS
              )
              labels_per_image[inds_to_discard] = -1.0
  
          labels.append(labels_per_image)
          matched_gt_boxes.append(matched_gt_boxes_per_image)
      return labels, matched_gt_boxes
  
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 4 for calling the RegionProposalNetwork obj's underlying member method code with example usage: obj.assign_targets_to_anchors(anchors, targets) and returns: labels,  matched_gt_boxes