code: def test_mnist_tutorial_cw(self):
      import tensorflow as tf
      from cleverhans_tutorials import mnist_tutorial_cw
  
      # Run the MNIST tutorial on a dataset of reduced size
      # and disable visualization.
      cw_tutorial_args = {
          "train_start": 0,
          "train_end": 10000,
          "test_start": 0,
          "test_end": 1666,
          "viz_enabled": False,
      }
      g = tf.Graph()
      with g.as_default():
          np.random.seed(42)
          report = mnist_tutorial_cw.mnist_tutorial_cw(
              **cw_tutorial_args
          )
  
      # Check accuracy values contained in the AccuracyReport object
      self.assertGreater(report.clean_train_clean_eval, 0.85)
      self.assertEqual(report.clean_train_adv_eval, 0.00)
  
      # There is no adversarial training in the CW tutorial
      self.assertEqual(report.adv_train_clean_eval, 0.0)
      self.assertEqual(report.adv_train_adv_eval, 0.0)
  
      g = tf.Graph()
      with g.as_default():
          np.random.seed(42)
          report_2 = mnist_tutorial_cw.mnist_tutorial_cw(
              **cw_tutorial_args
          )
  
      atol_fac = 1e-6
      self.assertClose(
          report.train_clean_train_clean_eval,
          report_2.train_clean_train_clean_eval,
          atol=atol_fac * 1,
      )
      self.assertClose(
          report.train_clean_train_adv_eval,
          report_2.train_clean_train_adv_eval,
          atol=atol_fac * 1,
      )
      self.assertClose(
          report.train_adv_train_clean_eval,
          report_2.train_adv_train_clean_eval,
          atol=atol_fac * 1,
      )
      self.assertClose(
          report.train_adv_train_adv_eval,
          report_2.train_adv_train_adv_eval,
          atol=atol_fac * 1,
      )
  
imports: import unittest
  import numpy
  from cleverhans.devtools.checks import CleverHansTest
label_desc: Use image node_id 1 for calling the TestMNISTTutorialCW obj's underlying member method code with example usage: obj.test_mnist_tutorial_cw() without return types