def METHOD_NAME(self):
    # Define the network
    model = Model()
    loss_fn = nn.CrossEntropyLoss()
    optimizer = optim.Adam(model.parameters(), lr=0.01)
    pixel_cnn = PyTorchClassifier(
        model=model, loss=loss_fn, optimizer=optimizer, input_shape=(4,), nb_classes=2, clip_values=(0, 1)
    )
    x = np.random.rand(5, 4).astype(np.float32)
    preprocess = PixelDefend(eps=5, pixel_cnn=pixel_cnn)
    x_defended, _ = preprocess(x)
    self.assertEqual(x_defended.shape, x.shape)
    self.assertTrue((x_defended <= 1.0).all())
    self.assertTrue((x_defended >= 0.0).all())