text: from copy import deepcopy
  from unittest import mock
  import tensorflow as tf
  
  
  def test_safe(func):
      """
      Isolate tests
      """
      def func_wrapper(*args):
          with tf.Graph().as_default():
              result = func(*args)
          print('Tests Passed')
          return result
  
      return func_wrapper
  
  
  def _assert_tensor_shape(tensor, shape, display_name):
      assert tf.assert_rank(tensor, len(shape), message='{} has wrong rank'.format(display_name))
  
      tensor_shape = tensor.get_shape().as_list() if len(shape) else []
  
      wrong_dimension = [ten_dim for ten_dim, cor_dim in zip(tensor_shape, shape)
                         if cor_dim is not None and ten_dim != cor_dim]
      assert not wrong_dimension, \
          '{} has wrong shape.  Found {}'.format(display_name, tensor_shape)
  
  
  def _check_input(tensor, shape, display_name, tf_name=None):
      assert tensor.op.type == 'Placeholder', \
          '{} is not a Placeholder.'.format(display_name)
  
      _assert_tensor_shape(tensor, shape, 'Real Input')
  
      if tf_name:
          assert tensor.name == tf_name, \
              '{} has bad name.  Found name {}'.format(display_name, tensor.name)
  
  
  class TmpMock():
      """
      Mock a attribute.  Restore attribute when exiting scope.
      """
      def __init__(self, module, attrib_name):
          self.original_attrib = deepcopy(getattr(module, attrib_name))
          setattr(module, attrib_name, mock.MagicMock())
          self.module = module
          self.attrib_name = attrib_name
  
      def __enter__(self):
          return getattr(self.module, self.attrib_name)
  
      def __exit__(self, type, value, traceback):
          setattr(self.module, self.attrib_name, self.original_attrib)
  
  
  @test_safe
  def test_model_inputs(model_inputs):
      image_width = 28
      image_height = 28
      image_channels = 3
      z_dim = 100
      input_real, input_z, learn_rate = model_inputs(image_width, image_height, image_channels, z_dim)
  
      _check_input(input_real, [None, image_width, image_height, image_channels], 'Real Input')
      _check_input(input_z, [None, z_dim], 'Z Input')
      _check_input(learn_rate, [], 'Learning Rate')
  
  
  @test_safe
  def test_discriminator(discriminator, tf_module):
      with TmpMock(tf_module, 'variable_scope') as mock_variable_scope:
          image = tf.placeholder(tf.float32, [None, 28, 28, 3])
  
          output, logits = discriminator(image)
          _assert_tensor_shape(output, [None, 1], 'Discriminator Training(reuse=false) output')
          _assert_tensor_shape(logits, [None, 1], 'Discriminator Training(reuse=false) Logits')
          assert mock_variable_scope.called,\
              'tf.variable_scope not called in Discriminator Training(reuse=false)'
          assert mock_variable_scope.call_args == mock.call('discriminator', reuse=False), \
              'tf.variable_scope called with wrong arguments in Discriminator Training(reuse=false)'
  
          mock_variable_scope.reset_mock()
  
          output_reuse, logits_reuse = discriminator(image, True)
          _assert_tensor_shape(output_reuse, [None, 1], 'Discriminator Inference(reuse=True) output')
          _assert_tensor_shape(logits_reuse, [None, 1], 'Discriminator Inference(reuse=True) Logits')
          assert mock_variable_scope.called, \
              'tf.variable_scope not called in Discriminator Inference(reuse=True)'
          assert mock_variable_scope.call_args == mock.call('discriminator', reuse=True), \
              'tf.variable_scope called with wrong arguments in Discriminator Inference(reuse=True)'
  
  
  @test_safe
  def test_generator(generator, tf_module):
      with TmpMock(tf_module, 'variable_scope') as mock_variable_scope:
          z = tf.placeholder(tf.float32, [None, 100])
          out_channel_dim = 5
  
          output = generator(z, out_channel_dim)
          _assert_tensor_shape(output, [None, 28, 28, out_channel_dim], 'Generator output (is_train=True)')
          assert mock_variable_scope.called, \
              'tf.variable_scope not called in Generator Training(reuse=false)'
          assert mock_variable_scope.call_args == mock.call('generator', reuse=False), \
              'tf.variable_scope called with wrong arguments in Generator Training(reuse=false)'
  
          mock_variable_scope.reset_mock()
          output = generator(z, out_channel_dim, False)
          _assert_tensor_shape(output, [None, 28, 28, out_channel_dim], 'Generator output (is_train=False)')
          assert mock_variable_scope.called, \
              'tf.variable_scope not called in Generator Inference(reuse=True)'
          assert mock_variable_scope.call_args == mock.call('generator', reuse=True), \
              'tf.variable_scope called with wrong arguments in Generator Inference(reuse=True)'
  
  
  @test_safe
  def test_model_loss(model_loss):
      out_channel_dim = 4
      input_real = tf.placeholder(tf.float32, [None, 28, 28, out_channel_dim])
      input_z = tf.placeholder(tf.float32, [None, 100])
  
      d_loss, g_loss = model_loss(input_real, input_z, out_channel_dim)
  
      _assert_tensor_shape(d_loss, [], 'Discriminator Loss')
      _assert_tensor_shape(d_loss, [], 'Generator Loss')
  
  
  @test_safe
  def test_model_opt(model_opt, tf_module):
      with TmpMock(tf_module, 'trainable_variables') as mock_trainable_variables:
          with tf.variable_scope('discriminator'):
              discriminator_logits = tf.Variable(tf.zeros([3, 3]))
          with tf.variable_scope('generator'):
              generator_logits = tf.Variable(tf.zeros([3, 3]))
  
          mock_trainable_variables.return_value = [discriminator_logits, generator_logits]
          d_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(
              logits=discriminator_logits,
              labels=[[0.0, 0.0, 1.0], [0.0, 1.0, 0.0], [1.0, 0.0, 0.0]]))
          g_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(
              logits=generator_logits,
              labels=[[0.0, 0.0, 1.0], [0.0, 1.0, 0.0], [1.0, 0.0, 0.0]]))
          learning_rate = 0.001
          beta1 = 0.9
  
          d_train_opt, g_train_opt = model_opt(d_loss, g_loss, learning_rate, beta1)
          assert mock_trainable_variables.called,\
              'tf.mock_trainable_variables not called'
  
  
  