code: def box_refinement(box, gt_box):
      """Compute refinement needed to transform box to gt_box.
      box and gt_box are [N, (y1, x1, y2, x2)] / 3D: (z1, z2))
      """
      height = box[:, 2] - box[:, 0]
      width = box[:, 3] - box[:, 1]
      center_y = box[:, 0] + 0.5 * height
      center_x = box[:, 1] + 0.5 * width
  
      gt_height = gt_box[:, 2] - gt_box[:, 0]
      gt_width = gt_box[:, 3] - gt_box[:, 1]
      gt_center_y = gt_box[:, 0] + 0.5 * gt_height
      gt_center_x = gt_box[:, 1] + 0.5 * gt_width
  
      dy = (gt_center_y - center_y) / height
      dx = (gt_center_x - center_x) / width
      dh = torch.log(gt_height / height)
      dw = torch.log(gt_width / width)
      result = torch.stack([dy, dx, dh, dw], dim=1)
  
      if box.shape[1] > 4:
          depth = box[:, 5] - box[:, 4]
          center_z = box[:, 4] + 0.5 * depth
          gt_depth = gt_box[:, 5] - gt_box[:, 4]
          gt_center_z = gt_box[:, 4] + 0.5 * gt_depth
          dz = (gt_center_z - center_z) / depth
          dd = torch.log(gt_depth / depth)
          result = torch.stack([dy, dx, dz, dh, dw, dd], dim=1)
  
      return result
  
imports: import numpy
  import scipy.misc
  import scipy.ndimage
  import torch
  from torch.autograd import Variable
  import torch.nn
label_desc: Use image node_id 4 for calling a global function with example usage: box_refinement(box, gt_box) and returns: result