class_docstr: This class encodes and decodes a set of bounding boxes into
  the representation used for training the regressors.
class_docstr_tok: ["This","class","encodes","and","decodes","a","set","of","bounding","boxes","into","the","representation","used","for","training","the","regressors","."]
docstr: From a set of original boxes and encoded relative box offsets,
  get the decoded boxes.
  
  Args:
      rel_codes (Tensor): encoded boxes
      boxes (Tensor): reference boxes.
docstr_tok: ["From","a","set","of","original","boxes","and","encoded","relative","box","offsets",",","get","the","decoded","boxes",".","Args",":","rel_codes","(","Tensor",")",":","encoded","boxes","boxes","(","Tensor",")",":","reference","boxes","."]
code: def decode_single(self, rel_codes: Tensor, boxes: Tensor) -> Tensor:
      """
      From a set of original boxes and encoded relative box offsets,
      get the decoded boxes.
  
      Args:
          rel_codes (Tensor): encoded boxes
          boxes (Tensor): reference boxes.
      """
  
      boxes = boxes.to(rel_codes.dtype)
  
      widths = boxes[:, 2] - boxes[:, 0]
      heights = boxes[:, 3] - boxes[:, 1]
      ctr_x = boxes[:, 0] + 0.5 * widths
      ctr_y = boxes[:, 1] + 0.5 * heights
  
      wx, wy, ww, wh = self.weights
      dx = rel_codes[:, 0::4] / wx
      dy = rel_codes[:, 1::4] / wy
      dw = rel_codes[:, 2::4] / ww
      dh = rel_codes[:, 3::4] / wh
  
      # Prevent sending too large values into torch.exp()
      dw = torch.clamp(dw, max=self.bbox_xform_clip)
      dh = torch.clamp(dh, max=self.bbox_xform_clip)
  
      pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
      pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
      pred_w = torch.exp(dw) * widths[:, None]
      pred_h = torch.exp(dh) * heights[:, None]
  
      # Distance from center to box's corner.
      c_to_c_h = (
          torch.tensor(
              0.5, dtype=pred_ctr_y.dtype, device=pred_h.device
          )
          * pred_h
      )
      c_to_c_w = (
          torch.tensor(
              0.5, dtype=pred_ctr_x.dtype, device=pred_w.device
          )
          * pred_w
      )
  
      pred_boxes1 = pred_ctr_x - c_to_c_w
      pred_boxes2 = pred_ctr_y - c_to_c_h
      pred_boxes3 = pred_ctr_x + c_to_c_w
      pred_boxes4 = pred_ctr_y + c_to_c_h
      pred_boxes = torch.stack(
          (pred_boxes1, pred_boxes2, pred_boxes3, pred_boxes4), dim=2
      ).flatten(1)
      return pred_boxes
  
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 5 for calling the BoxCoder obj's underlying member method code with example usage: obj.decode_single(rel_codes, boxes) and returns: pred_boxes