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: Arguments:
      weights (4-element tuple)
      bbox_xform_clip (float)
docstr_tok: ["Arguments",":","weights","(","4-element","tuple",")","bbox_xform_clip","(","float",")"]
code: def __init__(self, weights, bbox_xform_clip=math.log(1000.0 / 16)):
      # type: (Tuple[float, float, float, float], float) -> None
      """
      Arguments:
          weights (4-element tuple)
          bbox_xform_clip (float)
      """
      self.weights = weights
      self.bbox_xform_clip = bbox_xform_clip
  
imports: import torch
  import math
  from typing import List, Tuple
  from torch import Tensor
label_desc: Use image node_id 1 to create a new BoxCoder object from inherited base classes: object with example: obj = BoxCoder(weights, bbox_xform_clip)