code: def test_eot_zoom_blur_pytorch(art_warning, fix_get_mnist_subset):
      try:
          import torchvision
  
          if "+" in torchvision.__version__:
              torchvision_version = torchvision.__version__.split("+")[
                  0
              ]
          else:
              torchvision_version = torchvision.__version__
          torchvision_version = list(
              map(int, torchvision_version.lower().split("."))
          )
  
          if (
              torchvision_version[0] >= 0
              and torchvision_version[1] >= 8
          ):
              import torch
              from art.preprocessing.expectation_over_transformation.natural_corruptions.zoom_blur.pytorch import (
                  EoTZoomBlurPyTorch,
              )
  
              x_train_mnist, y_train_mnist, _, _ = fix_get_mnist_subset
              x_train_mnist = np.transpose(
                  x_train_mnist, (0, 2, 3, 1)
              )  # transpose to NHWC
  
              nb_samples = 3
  
              eot = EoTZoomBlurPyTorch(
                  nb_samples=nb_samples,
                  zoom=(1.5, 1.5),
                  clip_values=(0.0, 1.0),
              )
              x_eot, y_eot = eot.forward(
                  x=torch.from_numpy(x_train_mnist),
                  y=torch.from_numpy(y_train_mnist),
              )
  
              assert (
                  x_eot.shape[0] == nb_samples * x_train_mnist.shape[0]
              )
              assert (
                  y_eot.shape[0] == nb_samples * y_train_mnist.shape[0]
              )
  
              x_eot_expected = np.array(
                  [
                      0.0000000e00,
                      0.0000000e00,
                      0.0000000e00,
                      0.0000000e00,
                      0.0000000e00,
                      0.0000000e00,
                      0.0000000e00,
                      0.0000000e00,
                      0.0000000e00,
                      0.0000000e00,
                      9.3583600e-05,
                      6.7037535e-03,
                      7.0553131e-02,
                      4.1708022e-01,
                      8.4442711e-01,
                      9.2741704e-01,
                      8.8823336e-01,
                      6.6305310e-01,
                      3.1785199e-01,
                      1.1077325e-01,
                      2.7649008e-02,
                      4.9111363e-03,
                      0.0000000e00,
                      0.0000000e00,
                      0.0000000e00,
                      0.0000000e00,
                      0.0000000e00,
                      0.0000000e00,
                  ]
              )
  
              np.testing.assert_almost_equal(
                  x_eot.numpy()[0, 14, :, 0], x_eot_expected
              )
  
      except ARTTestException as e:
          art_warning(e)
  
imports: import logging
  import numpy
  import pytest
  from tests.utils import ARTTestException
label_desc: Use image node_id 2 for calling a global function with example usage: test_eot_zoom_blur_pytorch(art_warning, fix_get_mnist_subset) without return types