message: Some test cleanups.
diff: @@ -75,8 +75,8 @@ class GetGANModelTest(test.TestCase, parameterized.TestCase):
  def test_get_gan_model(self, mode):
  with ops.Graph().as_default():
  generator_inputs = {'x': array_ops.ones([3, 4])}
  - real_data = (array_ops.zeros([3, 4]) if
  - mode != model_fn_lib.ModeKeys.PREDICT else None)
  + is_predict = mode == model_fn_lib.ModeKeys.PREDICT
  + real_data = array_ops.zeros([3, 4]) if not is_predict else None
  gan_model = estimator._get_gan_model(
  mode, generator_fn, discriminator_fn, real_data, generator_inputs,
  add_summaries=False)
  @@ -139,6 +139,7 @@ class GetEstimatorSpecTest(test.TestCase, parameterized.TestCase):
  @classmethod
  def setUpClass(cls):
  + super(GetEstimatorSpecTest, cls).setUpClass()
  cls._generator_optimizer = training.GradientDescentOptimizer(1.0)
  cls._discriminator_optimizer = training.GradientDescentOptimizer(1.0)
  @@ -200,7 +201,6 @@ class GetEstimatorSpecTest(test.TestCase, parameterized.TestCase):
  self.assertSetEqual(frozenset(sync_opts), frozenset((g_opt, d_opt)))
  -# TODO(joelshor): Add pandas test.
  class GANEstimatorIntegrationTest(test.TestCase):
  def setUp(self):
  @@ -231,11 +231,11 @@ class GANEstimatorIntegrationTest(test.TestCase):
  get_eval_metric_ops_fn=get_metrics,
  model_dir=self._model_dir)
  - # TRAIN
  + # Train.
  num_steps = 10
  est.train(train_input_fn, steps=num_steps)
  - # EVALUTE
  + # Evaluate.
  scores = est.evaluate(eval_input_fn)
  self.assertEqual(num_steps, scores[ops.GraphKeys.GLOBAL_STEP])
  self.assertIn('loss', six.iterkeys(scores))
  @@ -243,7 +243,7 @@ class GANEstimatorIntegrationTest(test.TestCase):
  scores['loss'])
  self.assertIn('mse_custom_metric', six.iterkeys(scores))
  - # PREDICT
  + # Predict.
  predictions = np.array([x for x in est.predict(predict_input_fn)])
  self.assertAllEqual(prediction_size, predictions.shape)
  