code: def test_feature_vectors(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())
  
imports: from __future__ import absolute_import, division, print_function, unicode_literals
  import logging
  import unittest
  import numpy
  import torch.nn
  import torch.optim
  from art.estimators.classification.pytorch import PyTorchClassifier
  from art.defences.preprocessor import PixelDefend
  from art.utils import load_mnist
  from tests.utils import master_seed
label_desc: Use image node_id 3 for calling the TestPixelDefend obj's underlying member method code with example usage: obj.test_feature_vectors() without return types