code: def bbox2delta_v2(
      src_boxes,
      tgt_boxes,
      delta_mean=[0.0, 0.0, 0.0, 0.0],
      delta_std=[1.0, 1.0, 1.0, 1.0],
  ):
      """Encode bboxes to deltas.
      Modified from bbox2delta() which just use weight parameters to multiply deltas.
      """
      src_w = src_boxes[:, 2] - src_boxes[:, 0]
      src_h = src_boxes[:, 3] - src_boxes[:, 1]
      src_ctr_x = src_boxes[:, 0] + 0.5 * src_w
      src_ctr_y = src_boxes[:, 1] + 0.5 * src_h
  
      tgt_w = tgt_boxes[:, 2] - tgt_boxes[:, 0]
      tgt_h = tgt_boxes[:, 3] - tgt_boxes[:, 1]
      tgt_ctr_x = tgt_boxes[:, 0] + 0.5 * tgt_w
      tgt_ctr_y = tgt_boxes[:, 1] + 0.5 * tgt_h
  
      dx = (tgt_ctr_x - src_ctr_x) / src_w
      dy = (tgt_ctr_y - src_ctr_y) / src_h
      dw = paddle.log(tgt_w / src_w)
      dh = paddle.log(tgt_h / src_h)
  
      deltas = paddle.stack((dx, dy, dw, dh), axis=1)
      deltas = (
          deltas - paddle.to_tensor(delta_mean)
      ) / paddle.to_tensor(delta_std)
      return deltas
  
imports: import math
  import paddle
  import numpy
label_desc: Use image node_id 3 for calling a global function with example usage: bbox2delta_v2(src_boxes, tgt_boxes, delta_mean, delta_std) and returns: deltas