code: def disp_to_depth(disp, min_depth, max_depth):
      """Convert network's sigmoid output into depth prediction
      The formula for this conversion is given in the 'additional considerations'
      section of the paper.
      """
      min_disp = 1 / max_depth
      max_disp = 1 / min_depth
      scaled_disp = min_disp + (max_disp - min_disp) * disp
      depth = 1 / scaled_disp
      return scaled_disp, depth
  
imports: from __future__ import absolute_import, division, print_function
  import numpy
  import mxnet
  from mxnet.gluon import nn
label_desc: Use image node_id 1 for calling a global function with example usage: disp_to_depth(disp, min_depth, max_depth) and returns: scaled_disp,  depth