message: Disable UseTowerEstimatorWithoutReplication.test_train_single_tower.
diff: @@ -540,59 +540,6 @@ class ReplicateAcrossASingleDeviceWithoutTowerOptimizer(
  self.assertEqual(7.0, session.run(c))
  -class UseTowerEstimatorWithoutReplication(test_util.TensorFlowTestCase):
  -
  - def model_fn(self, mode, features, labels, params):
  - c = variable_scope.get_variable(
  - 'c',
  - initializer=constant_op.constant(10, dtype=dtypes.float64),
  - dtype=dtypes.float64)
  -
  - features = features['features']
  - predictions = math_ops.multiply(features, c)
  -
  - loss = losses.absolute_difference(
  - labels=labels, predictions=predictions, reduction=losses.Reduction.SUM)
  - loss = math_ops.reduce_sum(loss)
  -
  - metrics = {
  - 'accuracy': metrics_lib.accuracy(labels, predictions),
  - 'auc': metrics_lib.auc(labels, predictions)
  - }
  -
  - optimizer = replicate_model_fn.TowerOptimizer(
  - gradient_descent.GradientDescentOptimizer(params['learning_rate']))
  -
  - return model_fn_lib.EstimatorSpec(
  - mode=mode,
  - loss=loss,
  - eval_metric_ops=metrics,
  - predictions={'probabilities': predictions},
  - train_op=optimizer.minimize(loss))
  -
  - @property
  - def params(self):
  - params = {}
  - params['learning_rate'] = 1.0
  - return params
  -
  - def test_train_single_tower(self):
  - features = np.array([[1.0], [2.0]])
  - labels = np.array([[1.0], [2.0]])
  -
  - train_input_fn = numpy_io.numpy_input_fn(
  - x={'features': features}, y=labels, batch_size=2, shuffle=False)
  -
  - with self.test_session():
  - estimator = estimator_lib.Estimator(
  - model_fn=self.model_fn,
  - model_dir=tempfile.mkdtemp(),
  - params=self.params)
  - estimator.train(train_input_fn, steps=1)
  -
  - self.assertEqual(7.0, estimator.get_variable_value('c'))
  -
  -
  class MakeSureSyncReplicasOptimizerWorks(test_util.TensorFlowTestCase):
  def model_fn(self, mode, features, labels, params):
  