code: def decode_yolo(box, anchor, downsample_ratio):
      """decode yolo box
  
      Args:
          box (list): [x, y, w, h], all have the shape [b, na, h, w, 1]
          anchor (list): anchor with the shape [na, 2]
          downsample_ratio (int): downsample ratio, default 32
          scale (float): scale, default 1.
  
      Return:
          box (list): decoded box, [x, y, w, h], all have the shape [b, na, h, w, 1]
      """
      x, y, w, h = box
      na, grid_h, grid_w = x.shape[1:4]
      grid = make_grid(grid_h, grid_w, x.dtype).reshape(
          (1, 1, grid_h, grid_w, 2)
      )
      x1 = (x + grid[:, :, :, :, 0:1]) / grid_w
      y1 = (y + grid[:, :, :, :, 1:2]) / grid_h
  
      anchor = paddle.to_tensor(anchor, dtype=x.dtype)
      anchor = anchor.reshape((1, na, 1, 1, 2))
      w1 = (
          paddle.exp(w)
          * anchor[:, :, :, :, 0:1]
          / (downsample_ratio * grid_w)
      )
      h1 = (
          paddle.exp(h)
          * anchor[:, :, :, :, 1:2]
          / (downsample_ratio * grid_h)
      )
  
      return [x1, y1, w1, h1]
  
imports: import math
  import paddle
  import numpy
label_desc: Use image node_id 13 for calling a global function with example usage: decode_yolo(box, anchor, downsample_ratio) and returns: list