code: def get_mnist_lenet5_advtrained():
      filename = "mnist_lenet5_advtrained.pt"
      model = LeNet5()
      model.load_state_dict(
          torch.load(os.path.join(TRAINED_MODEL_PATH, filename))
      )
      model.eval()
      model.name = (
          "MNIST LeNet 5 PGD training according to Madry et al. 2018"
      )
      # TODO: also described where can you find this model, and how is it trained
      return model
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from __future__ import unicode_literals
  import os
  import sys
  import pathlib
  from torch.utils.data.dataset import Subset
  import numpy
  import torch
  import torchvision.transforms
  import torchvision.datasets
  from advertorch.test_utils import LeNet5
label_desc: Use image node_id 7 for calling a global function with example usage: get_mnist_lenet5_advtrained() and returns: model