class_docstr: This class represents a bounding box detection in a single image.
  
  Args:
      tlwh (Tensor): Bounding box in format `(top left x, top left y,
          width, height)`.
      score (Tensor): Bounding box confidence score.
      feature (Tensor): A feature vector that describes the object 
          contained in this image.
      cls_id (Tensor): Bounding box category id.
class_docstr_tok: ["This","class","represents","a","bounding","box","detection","in","a","single","image",".","Args",":","tlwh","(","Tensor",")",":","Bounding","box","in","format","`","(","top","left","x",",","top","left","y",",","width",",","height",")","`",".","score","(","Tensor",")",":","Bounding","box","confidence","score",".","feature","(","Tensor",")",":","A","feature","vector","that","describes","the","object","contained","in","this","image",".","cls_id","(","Tensor",")",":","Bounding","box","category","id","."]
docstr: Convert bounding box to format `(min x, min y, max x, max y)`, i.e.,
  `(top left, bottom right)`.
docstr_tok: ["Convert","bounding","box","to","format","`","(","min","x",",","min","y",",","max","x",",","max","y",")","`",",","i.e.",",","`","(","top","left",",","bottom","right",")","`","."]
code: def to_tlbr(self):
      """
      Convert bounding box to format `(min x, min y, max x, max y)`, i.e.,
      `(top left, bottom right)`.
      """
      ret = self.tlwh.copy()
      ret[2:] += ret[:2]
      return ret
  
imports: import os
  import cv2
  import time
  import numpy
  import collections
  import math
label_desc: Use image node_id 2 for calling the Detection obj's underlying member method code with example usage: obj.to_tlbr() and returns: ret