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: Args:
      weights (4-element tuple)
      bbox_xform_clip (float)
docstr_tok: ["Args",":","weights","(","4-element","tuple",")","bbox_xform_clip","(","float",")"]
code: def __init__(
      self,
      weights: Tuple[float, float, float, float],
      bbox_xform_clip: float = math.log(1000.0 / 16),
  ) -> None:
      """
      Args:
          weights (4-element tuple)
          bbox_xform_clip (float)
      """
      self.weights = weights
      self.bbox_xform_clip = bbox_xform_clip
  
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 to create a new BoxCoder object  with example: obj = BoxCoder(weights, bbox_xform_clip)