class_docstr: The linear box-to-box transform defined in FCOS. The transformation is parameterized
  by the distance from the center of (square) src box to 4 edges of the target box.
class_docstr_tok: ["The","linear","box-to-box","transform","defined","in","FCOS",".","The","transformation","is","parameterized","by","the","distance","from","the","center","of","(","square",")","src","box","to","4","edges","of","the","target","box","."]
docstr: Args:
      normalize_by_size: normalize deltas by the size of src (anchor) boxes.
docstr_tok: ["Args",":","normalize_by_size",":","normalize","deltas","by","the","size","of","src","(","anchor",")","boxes","."]
code: def __init__(self, normalize_by_size=True):
      """
      Args:
          normalize_by_size: normalize deltas by the size of src (anchor) boxes.
      """
      self.normalize_by_size = normalize_by_size
  
file_path: detectron2/detectron2/modeling/box_regression.py
imports: import math
  from typing import List, Tuple, Union
  import torch
  from fvcore.nn import giou_loss, smooth_l1_loss
  from torch.nn import functional
  from detectron2.layers import cat, ciou_loss, diou_loss
  from detectron2.structures import Boxes
label_desc: Use image node_id 1 to create a new Box2BoxTransformLinear object  with example: obj = Box2BoxTransformLinear(normalize_by_size)