code: def test_smoothmix_mnist_predict(
      art_warning, get_default_mnist_subset, get_mnist_classifier
  ):
      (_, _), (x_test, y_test) = get_default_mnist_subset
      x_test, y_test = x_test[:10], y_test[:10]
  
      try:
          classifier, _, rs = get_mnist_classifier()
          y_test_base = classifier.predict(x=x_test)
          y_test_smooth = rs.predict(x=x_test)
  
          np.testing.assert_array_equal(
              y_test_smooth.shape, y_test_base.shape
          )
          np.testing.assert_array_almost_equal(
              np.sum(y_test_smooth, axis=1), np.ones(len(y_test))
          )
          np.testing.assert_array_almost_equal(
              np.argmax(y_test_smooth, axis=1),
              np.argmax(y_test_base, axis=1),
          )
  
      except ARTTestException as e:
          art_warning(e)
  
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 3 for calling a global function with example usage: test_smoothmix_mnist_predict(art_warning, get_default_mnist_subset, get_mnist_classifier) without return types