code: def get_mnist_classifier(framework):
      def _get_classifier():
          if framework == "pytorch":
              import torch
  
              classifier = get_image_classifier_pt()
              optimizer = torch.optim.SGD(
                  classifier.model.parameters(),
                  lr=0.1,
                  momentum=0.9,
                  weight_decay=1e-4,
              )
              scheduler = torch.optim.lr_scheduler.StepLR(
                  optimizer=optimizer, step_size=50, gamma=0.1
              )
              rs = PyTorchSmoothMix(
                  model=classifier.model,
                  loss=classifier._loss,
                  input_shape=classifier.input_shape,
                  nb_classes=classifier.nb_classes,
                  optimizer=optimizer,
                  clip_values=classifier.clip_values,
                  channels_first=classifier.channels_first,
                  sample_size=100,
                  scale=0.01,
                  alpha=0.001,
                  eta=5.0,
                  num_noise_vec=2,
                  num_steps=8,
                  warmup=10,
                  mix_step=0,
                  maxnorm_s=None,
                  maxnorm=None,
              )
  
          else:
              classifier, scheduler, rs = None, None, None
  
          return classifier, scheduler, rs
  
      return _get_classifier
  
file_path: adversarial-robustness-toolbox/tests/estimators/certification/test_smooth_mix.py
imports: from __future__ import absolute_import, division, print_function, unicode_literals
  import logging
  import pytest
  import numpy
  from art.estimators.certification.randomized_smoothing import PyTorchSmoothMix
  from tests.utils import ARTTestException, get_image_classifier_pt, get_cifar10_image_classifier_pt
label_desc: Use image node_id 1 for calling a global function with example usage: get_mnist_classifier(framework) and returns: _get_classifier, classifier,  scheduler,  rs