code: def get_smooth_loss(disp, img):
      """Computes the smoothness loss for a disparity image
      The color image is used for edge-aware smoothness
      """
      grad_disp_x = mx.nd.abs(disp[:, :, :, :-1] - disp[:, :, :, 1:])
      grad_disp_y = mx.nd.abs(disp[:, :, :-1, :] - disp[:, :, 1:, :])
  
      grad_img_x = mx.nd.mean(
          data=mx.nd.abs(img[:, :, :, :-1] - img[:, :, :, 1:]),
          axis=1,
          keepdims=True,
      )
      grad_img_y = mx.nd.mean(
          data=mx.nd.abs(img[:, :, :-1, :] - img[:, :, 1:, :]),
          axis=1,
          keepdims=True,
      )
  
      grad_disp_x = grad_disp_x * mx.nd.exp(-grad_img_x)
      grad_disp_y = grad_disp_y * mx.nd.exp(-grad_img_y)
  
      return grad_disp_x.mean() + grad_disp_y.mean()
  
imports: from __future__ import absolute_import, division, print_function
  import numpy
  import mxnet
  from mxnet.gluon import nn
label_desc: Use image node_id 5 for calling a global function with example usage: get_smooth_loss(disp, img) and returns: unknown