message: Fix SavedModelEstimator docstring formatting.
diff: @@ -46,6 +46,7 @@ class SavedModelEstimator(estimator_lib.Estimator):
  Example with `tf.estimator.DNNClassifier`:
  **Step 1: Create and train DNNClassifier.**
  +
  ```python
  feature1 = tf.feature_column.embedding_column(
  tf.feature_column.categorical_column_with_vocabulary_list(
  @@ -66,6 +67,7 @@ class SavedModelEstimator(estimator_lib.Estimator):
  **Step 2: Export classifier.**
  First, build functions that specify the expected inputs.
  +
  ```python
  # During train and evaluation, both the features and labels should be defined.
  supervised_input_receiver_fn = (
  @@ -83,6 +85,7 @@ class SavedModelEstimator(estimator_lib.Estimator):
  Next, export the model as a SavedModel. A timestamped directory will be
  created (for example `/tmp/export_all/1234567890`).
  +
  ```python
  # Option 1: Save all modes (train, eval, predict)
  export_dir = tf.contrib.estimator.export_all_saved_models(
  @@ -97,6 +100,7 @@ class SavedModelEstimator(estimator_lib.Estimator):
  ```
  **Step 3: Create a SavedModelEstimator from the exported SavedModel.**
  +
  ```python
  est = tf.contrib.estimator.SavedModelEstimator(export_dir)
  @@ -108,7 +112,7 @@ class SavedModelEstimator(estimator_lib.Estimator):
  est.train(input_fn=input_fn, steps=20)
  def predict_input_fn():
  - example = example_pb2.Example()
  + example = tf.train.Example()
  example.features.feature['feature1'].bytes_list.value.extend(['yellow'])
  example.features.feature['feature2'].float_list.value.extend([1.])
  return {'inputs':tf.constant([example.SerializeToString()])}
  