code: def test_device(art_warning):
      try:
  
          class Flatten(nn.Module):
              def forward(self, x):
                  n, _, _, _ = x.size()
                  result = x.view(n, -1)
  
                  return result
  
          # Define the network
          model = nn.Sequential(
              nn.Conv2d(1, 2, 5),
              nn.ReLU(),
              nn.MaxPool2d(2, 2),
              Flatten(),
              nn.Linear(288, 10),
          )
  
          # Define a loss function and optimizer
          loss_fn = nn.CrossEntropyLoss()
          optimizer = optim.Adam(model.parameters(), lr=0.01)
  
          # First test cpu
          classifier_cpu = PyTorchClassifier(
              model=model,
              clip_values=(0, 1),
              loss=loss_fn,
              optimizer=optimizer,
              input_shape=(1, 28, 28),
              nb_classes=10,
              device_type="cpu",
          )
  
          assert classifier_cpu._device == torch.device("cpu")
          assert classifier_cpu._device != torch.device("cuda")
  
          # Then test gpu
          if torch.cuda.device_count() >= 2:
              with torch.cuda.device(0):
                  classifier_gpu0 = PyTorchClassifier(
                      model=model,
                      clip_values=(0, 1),
                      loss=loss_fn,
                      optimizer=optimizer,
                      input_shape=(1, 28, 28),
                      nb_classes=10,
                  )
                  assert classifier_gpu0._device == torch.device(
                      "cuda:0"
                  )
                  assert classifier_gpu0._device != torch.device(
                      "cuda:1"
                  )
  
              with torch.cuda.device(1):
                  classifier_gpu1 = PyTorchClassifier(
                      model=model,
                      clip_values=(0, 1),
                      loss=loss_fn,
                      optimizer=optimizer,
                      input_shape=(1, 28, 28),
                      nb_classes=10,
                  )
                  assert classifier_gpu1._device == torch.device(
                      "cuda:1"
                  )
                  assert classifier_gpu1._device != torch.device(
                      "cuda:0"
                  )
      except ARTTestException as e:
          art_warning(e)
  
file_path: adversarial-robustness-toolbox/tests/estimators/classification/test_deeplearning_specific.py
imports: import os
  import logging
  import numpy
  import pytest
  import pickle
  import torch
  import torch.nn
  import torch.nn.functional
  import torch.optim
  from art.estimators.classification.pytorch import PyTorchClassifier
  from tests.utils import ARTTestException
label_desc: Use image node_id 1 for calling a global function with example usage: test_device(art_warning) and returns: result