code: def delta2bbox(
      deltas, boxes, weights=[1.0, 1.0, 1.0, 1.0], max_shape=None
  ):
      """Decode deltas to boxes. Used in RCNNBox,CascadeHead,RCNNHead,RetinaHead.
      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
  
      wx, wy, ww, wh = weights
      dx = deltas[:, 0::4] / wx
      dy = deltas[:, 1::4] / wy
      dw = deltas[:, 2::4] / ww
      dh = deltas[:, 3::4] / wh
      # Prevent sending too large values into paddle.exp()
      dw = paddle.clip(dw, max=clip_scale)
      dh = paddle.clip(dh, max=clip_scale)
  
      pred_ctr_x = dx * widths.unsqueeze(1) + ctr_x.unsqueeze(1)
      pred_ctr_y = dy * heights.unsqueeze(1) + 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 2 for calling a global function with example usage: delta2bbox(deltas, boxes, weights, max_shape) and returns: pred_boxes