code: def delta2bbox_v2(
      deltas,
      boxes,
      delta_mean=[0.0, 0.0, 0.0, 0.0],
      delta_std=[1.0, 1.0, 1.0, 1.0],
      max_shape=None,
      ctr_clip=32.0,
  ):
      """Decode deltas to bboxes.
      Modified from delta2bbox() which just use weight parameters to be divided by deltas.
      Used in YOLOFHead.
      Note: return tensor shape [n,1,4]
          If you want to add a reshape, please add after the calling code instead of here.
      """
      clip_scale = math.log(1000.0 / 16)
  
      widths = boxes[:, 2] - boxes[:, 0]
      heights = boxes[:, 3] - boxes[:, 1]
      ctr_x = boxes[:, 0] + 0.5 * widths
      ctr_y = boxes[:, 1] + 0.5 * heights
  
      deltas = deltas * paddle.to_tensor(delta_std) + paddle.to_tensor(
          delta_mean
      )
      dx = deltas[:, 0::4]
      dy = deltas[:, 1::4]
      dw = deltas[:, 2::4]
      dh = deltas[:, 3::4]
  
      # Prevent sending too large values into paddle.exp()
      dx = dx * widths.unsqueeze(1)
      dy = dy * heights.unsqueeze(1)
      if ctr_clip is not None:
          dx = paddle.clip(dx, max=ctr_clip, min=-ctr_clip)
          dy = paddle.clip(dy, max=ctr_clip, min=-ctr_clip)
          dw = paddle.clip(dw, max=clip_scale)
          dh = paddle.clip(dh, max=clip_scale)
      else:
          dw = dw.clip(min=-clip_scale, max=clip_scale)
          dh = dh.clip(min=-clip_scale, max=clip_scale)
  
      pred_ctr_x = dx + ctr_x.unsqueeze(1)
      pred_ctr_y = dy + ctr_y.unsqueeze(1)
      pred_w = paddle.exp(dw) * widths.unsqueeze(1)
      pred_h = paddle.exp(dh) * heights.unsqueeze(1)
  
      pred_boxes = []
      pred_boxes.append(pred_ctr_x - 0.5 * pred_w)
      pred_boxes.append(pred_ctr_y - 0.5 * pred_h)
      pred_boxes.append(pred_ctr_x + 0.5 * pred_w)
      pred_boxes.append(pred_ctr_y + 0.5 * pred_h)
      pred_boxes = paddle.stack(pred_boxes, axis=-1)
  
      if max_shape is not None:
          pred_boxes[..., 0::2] = pred_boxes[..., 0::2].clip(
              min=0, max=max_shape[1]
          )
          pred_boxes[..., 1::2] = pred_boxes[..., 1::2].clip(
              min=0, max=max_shape[0]
          )
      return pred_boxes
  
imports: import math
  import paddle
  import numpy
label_desc: Use image node_id 4 for calling a global function with example usage: delta2bbox_v2(deltas, boxes, delta_mean, delta_std, max_shape, ctr_clip) and returns: pred_boxes