code: def test_one_channel(self):
      (x_train, _), (_, _), _, _ = load_mnist()
      x_train = x_train[:2, 10:15, 15:20, :]
      x_train = x_train.astype(np.float32)
      x_train_original = x_train.copy()
  
      # Define the network
      model = ModelImage()
      loss_fn = nn.CrossEntropyLoss()
      optimizer = optim.Adam(model.parameters(), lr=0.01)
      self.pixelcnn = PyTorchClassifier(
          model=model,
          loss=loss_fn,
          optimizer=optimizer,
          input_shape=(1, 28, 28),
          nb_classes=10,
          clip_values=(0, 1),
      )
      preprocess = PixelDefend(eps=5, pixel_cnn=self.pixelcnn)
      x_defended, _ = preprocess(x_train)
  
      self.assertEqual(x_defended.shape, x_train.shape)
      self.assertTrue((x_defended <= 1.0).all())
      self.assertTrue((x_defended >= 0.0).all())
  
      # Check that x_train has not been modified by attack and classifier
      self.assertAlmostEqual(
          float(np.max(np.abs(x_train_original - x_train))),
          0.0,
          delta=0.00001,
      )
  
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 2 for calling the TestPixelDefend obj's underlying member method code with example usage: obj.test_one_channel() without return types