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.
docstr: Convert bounding box to format `(center x, center y, aspect ratio,
  height)`, where the aspect ratio is `width / height`.
docstr_tok: ["Convert","bounding","box","to","format","`","(","center","x",",","center","y",",","aspect","ratio",",","height",")","`",",","where","the","aspect","ratio","is","`","width","\/","height","`","."]
code: def to_xyah(self):
      """
      Convert bounding box to format `(center x, center y, aspect ratio,
      height)`, where the aspect ratio is `width / height`.
      """
      ret = self.tlwh.copy()
      ret[:2] += ret[2:] / 2
      ret[2] /= ret[3]
      return ret
  
imports: import os
  import cv2
  import time
  import numpy
  from .visualization import plot_tracking_dict, plot_tracking
label_desc: Use image node_id 3 for calling the Detection obj's underlying member method code with example usage: obj.to_xyah() and returns: ret