code: def test_mnist_blackbox(self):
      import tensorflow as tf
      from cleverhans_tutorials import mnist_blackbox
  
      # Run the MNIST tutorial on a dataset of reduced size, reduced number
      # of data augmentations, increased substitute holdout for faster runtime.
      mnist_blackbox_args = {
          "train_start": 0,
          "train_end": 5000,
          "test_start": 0,
          "test_end": 2000,
          "data_aug": 1,
          "holdout": 1000,
          "nb_epochs": 2,
          "nb_epochs_s": 6,
      }
      g = tf.Graph()
      with g.as_default():
          np.random.seed(42)
          report = mnist_blackbox.mnist_blackbox(**mnist_blackbox_args)
  
      # Check accuracy values contained in the AccuracyReport object
      self.assertTrue(report["bbox"] > 0.7, report["bbox"])
      self.assertTrue(report["sub"] > 0.7, report["sub"])
      self.assertTrue(
          report["bbox_on_sub_adv_ex"] < 0.3,
          report["bbox_on_sub_adv_ex"],
      )
  
      g = tf.Graph()
      with g.as_default():
          np.random.seed(42)
          report_2 = mnist_blackbox.mnist_blackbox(
              **mnist_blackbox_args
          )
  
      atol_fac = 1e-2 if HAS_GPU else 1e-6
      self.assertClose(
          report["bbox"], report_2["bbox"], atol=atol_fac * 1
      )
      self.assertClose(
          report["sub"], report_2["sub"], atol=atol_fac * 1
      )
      self.assertClose(
          report["bbox_on_sub_adv_ex"],
          report_2["bbox_on_sub_adv_ex"],
          atol=atol_fac * 1,
      )
  
imports: import unittest
  import numpy
  from tensorflow.python.client import device_lib
  from cleverhans.devtools.checks import CleverHansTest
label_desc: Use image node_id 1 for calling the TestMNISTBlackboxF obj's underlying member method code with example usage: obj.test_mnist_blackbox() without return types