code: def mnist_cnn_model(img):
      """
      Mnist cnn model
  
      Args:
          img(Varaible): the input image to be recognized
  
      Returns:
          Variable: the label prediction
      """
      conv_pool_1 = fluid.nets.simple_img_conv_pool(
          input=img,
          num_filters=20,
          filter_size=5,
          pool_size=2,
          pool_stride=2,
          act="relu",
      )
  
      conv_pool_2 = fluid.nets.simple_img_conv_pool(
          input=conv_pool_1,
          num_filters=50,
          filter_size=5,
          pool_size=2,
          pool_stride=2,
          act="relu",
      )
      fc = fluid.layers.fc(input=conv_pool_2, size=50, act="relu")
  
      logits = fluid.layers.fc(input=fc, size=10, act="softmax")
      return logits
  
file_path: AdvBox/tutorials/mnist_model_gaussian_augmentation_defence.py
imports: from __future__ import print_function
  from builtins import str
  from builtins import range
  import sys
  import os
  import logging
  import paddle.v2
  import paddle.fluid
  import numpy
  from adversarialbox.defences.gaussian_augmentation import GaussianAugmentationDefence
label_desc: Use image node_id 1 for calling a global function with example usage: mnist_cnn_model(img) and returns: logits