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","."]
code: def decode(self, rel_codes: Tensor, boxes: List[Tensor]) -> Tensor:
      torch._assert(
          isinstance(boxes, (list, tuple)),
          "This function expects boxes of type list or tuple.",
      )
      torch._assert(
          isinstance(rel_codes, torch.Tensor),
          "This function expects rel_codes of type torch.Tensor.",
      )
      boxes_per_image = [b.size(0) for b in boxes]
      concat_boxes = torch.cat(boxes, dim=0)
      box_sum = 0
      for val in boxes_per_image:
          box_sum += val
      if box_sum > 0:
          rel_codes = rel_codes.reshape(box_sum, -1)
      pred_boxes = self.decode_single(rel_codes, concat_boxes)
      if box_sum > 0:
          pred_boxes = pred_boxes.reshape(box_sum, -1, 4)
      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 4 for calling the BoxCoder obj's underlying member method code with example usage: obj.decode(rel_codes, boxes) and returns: pred_boxes