code: def get_conv_out_grad(
      net, image, class_id=None, conv_layer_name=None
  ):
      """Get the output and gradients of output of a convolutional layer.
  
      Parameters:
      ----------
      net: Block
          Network to use for visualization.
      image: NDArray
          Preprocessed image to use for visualization.
      class_id: int
          Category ID this image belongs to. If not provided,
          network's prediction will be used.
      conv_layer_name: str
          Name of the convolutional layer whose output and output's gradients need to be acptured.
      """
      return _get_grad(
          net, image, class_id, conv_layer_name, image_grad=False
      )
  
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 4 for calling a global function with example usage: get_conv_out_grad(net, image, class_id, conv_layer_name) and returns: _get_grad