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: Encode a set of proposals with respect to some
  reference boxes
  
  Args:
      reference_boxes (Tensor): reference boxes
      proposals (Tensor): boxes to be encoded
docstr_tok: ["Encode","a","set","of","proposals","with","respect","to","some","reference","boxes","Args",":","reference_boxes","(","Tensor",")",":","reference","boxes","proposals","(","Tensor",")",":","boxes","to","be","encoded"]
code: def encode_single(
      self, reference_boxes: Tensor, proposals: 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
      """
      dtype = reference_boxes.dtype
      device = reference_boxes.device
      weights = torch.as_tensor(
          self.weights, dtype=dtype, device=device
      )
      targets = encode_boxes(reference_boxes, proposals, weights)
  
      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 3 for calling the BoxCoder obj's underlying member method code with example usage: obj.encode_single(reference_boxes, proposals) and returns: targets