code: def to_grayscale(cv2im):
      """Convert gradients to grayscale. This gives a saliency map."""
      # How strongly does each position activate the output
      grayscale_im = onp.sum(onp.abs(cv2im), axis=0)
  
      # Normalize between min and 99th percentile
      im_max = onp.percentile(grayscale_im, 99)
      im_min = onp.min(grayscale_im)
      grayscale_im = onp.clip(
          (grayscale_im - im_min) / (im_max - im_min), 0, 1
      )
  
      grayscale_im = onp.expand_dims(grayscale_im, axis=0)
      return grayscale_im
  
imports: from __future__ import print_function
  import mxnet
  from mxnet import gluon, np, npx
  from mxnet import autograd
  from mxnet.gluon import nn
  import numpy
  import cv2
label_desc: Use image node_id 10 for calling a global function with example usage: to_grayscale(cv2im) and returns: grayscale_im