code: def encode_boxes(
      reference_boxes: Tensor, proposals: Tensor, weights: Tensor
  ) -> Tensor:
      """
      Encode a set of proposals with respect to some
      reference boxes
  
      Args:
          reference_boxes (Tensor): reference boxes
          proposals (Tensor): boxes to be encoded
          weights (Tensor[4]): the weights for ``(x, y, w, h)``
      """
  
      # perform some unpacking to make it JIT-fusion friendly
      wx = weights[0]
      wy = weights[1]
      ww = weights[2]
      wh = weights[3]
  
      proposals_x1 = proposals[:, 0].unsqueeze(1)
      proposals_y1 = proposals[:, 1].unsqueeze(1)
      proposals_x2 = proposals[:, 2].unsqueeze(1)
      proposals_y2 = proposals[:, 3].unsqueeze(1)
  
      reference_boxes_x1 = reference_boxes[:, 0].unsqueeze(1)
      reference_boxes_y1 = reference_boxes[:, 1].unsqueeze(1)
      reference_boxes_x2 = reference_boxes[:, 2].unsqueeze(1)
      reference_boxes_y2 = reference_boxes[:, 3].unsqueeze(1)
  
      # implementation starts here
      ex_widths = proposals_x2 - proposals_x1
      ex_heights = proposals_y2 - proposals_y1
      ex_ctr_x = proposals_x1 + 0.5 * ex_widths
      ex_ctr_y = proposals_y1 + 0.5 * ex_heights
  
      gt_widths = reference_boxes_x2 - reference_boxes_x1
      gt_heights = reference_boxes_y2 - reference_boxes_y1
      gt_ctr_x = reference_boxes_x1 + 0.5 * gt_widths
      gt_ctr_y = reference_boxes_y1 + 0.5 * gt_heights
  
      targets_dx = wx * (gt_ctr_x - ex_ctr_x) / ex_widths
      targets_dy = wy * (gt_ctr_y - ex_ctr_y) / ex_heights
      targets_dw = ww * torch.log(gt_widths / ex_widths)
      targets_dh = wh * torch.log(gt_heights / ex_heights)
  
      targets = torch.cat(
          (targets_dx, targets_dy, targets_dw, targets_dh), dim=1
      )
      return targets
  
imports: import math
  from collections import OrderedDict
  from typing import Dict, List, Optional, Tuple
  import torch
  from torch import nn, Tensor
  from torch.nn import functional
  from torchvision.ops import complete_box_iou_loss, distance_box_iou_loss, FrozenBatchNorm2d, generalized_box_iou_loss
label_desc: Use image node_id 1 for calling a global function with example usage: encode_boxes(reference_boxes, proposals, weights) and returns: targets