code: def test_pytorch_binary_pgd(art_warning, get_mnist_dataset):
      """
      This test instantiates a binary classification PyTorch model, then attacks it using PGD
  
      """
  
      class BasicModel(nn.Module):
          def __init__(self):
              super(BasicModel, self).__init__()
              self.layer_1 = nn.Linear(20, 32)
              self.layer_2 = nn.Linear(32, 1)
  
          def forward(self, x):
              x = F.relu(self.layer_1(x))
              x = torch.sigmoid(self.layer_2(x))
  
              return x
  
      try:
          device = "cpu"
          x, y = sklearn.datasets.make_classification(
              n_samples=10000,
              n_features=20,
              n_informative=5,
              n_redundant=2,
              n_repeated=0,
              n_classes=2,
          )
          (
              train_x,
              test_x,
              train_y,
              test_y,
          ) = sklearn.model_selection.train_test_split(
              x, y, test_size=0.2
          )
          train_x = train_x.astype(np.float32)
          train_y = train_y.astype(np.float32)
          test_x = test_x.astype(np.float32)
          model = BasicModel()
          loss_func = nn.BCELoss()
          model.to(device)
          opt = optim.Adam(model.parameters(), lr=0.001)
          classifier = PyTorchClassifier(
              model=model,
              loss=loss_func,
              optimizer=opt,
              input_shape=(1, 28, 28),
              nb_classes=2,
          )
          classifier.fit(train_x, train_y, batch_size=64, nb_epochs=3)
          test_x_batch = test_x[0:16]
          preds = classifier.predict(test_x_batch)
          attacker = ProjectedGradientDescent(classifier, eps=0.5)
          generated = attacker.generate(test_x_batch)
          adv_predicted = classifier.predict(generated)
          assert (adv_predicted != preds).all()
      except ARTTestException as e:
          art_warning(e)
  
imports: import numpy
  import pytest
  import torch
  import torch.nn.functional
  import torch.nn
  import torch.optim
  import sklearn.datasets
  from art.estimators.classification.pytorch import PyTorchClassifier
  from art.defences.preprocessor.spatial_smoothing import SpatialSmoothing
  from art.defences.preprocessor.spatial_smoothing_pytorch import SpatialSmoothingPyTorch
  from art.attacks.evasion.fast_gradient import FastGradientMethod
  from art.attacks.evasion.projected_gradient_descent.projected_gradient_descent import ProjectedGradientDescent
  from tests.attacks.utils import backend_test_defended_images
  from tests.utils import ARTTestException
label_desc: Use image node_id 10 for calling a global function with example usage: test_pytorch_binary_pgd(art_warning, get_mnist_dataset) and returns: x