Fix SavedModelEstimator docstring formatting.

PiperOrigin-RevId: 206361654

1 files changed, 9 insertions, 5 deletions

diff --git a/tensorflow/contrib/estimator/python/estimator/saved_model_estimator.py b/tensorflow/contrib/estimator/python/estimator/saved_model_estimator.py
index f3d0f6b047..b0082f7e55 100644
--- a/tensorflow/contrib/estimator/python/estimator/saved_model_estimator.py
+++ b/tensorflow/contrib/estimator/python/estimator/saved_model_estimator.py
@@ -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(
@@ -66,13 +67,14 @@ 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 = (
       tf.contrib.estimator.build_raw_supervised_input_receiver_fn(
-        {'feature1': tf.placeholder(dtype=tf.string, shape=[None]),
-         'feature2': tf.placeholder(dtype=tf.float32, shape=[None])},
-        tf.placeholder(dtype=tf.float32, shape=[None])))
+          {'feature1': tf.placeholder(dtype=tf.string, shape=[None]),
+           'feature2': tf.placeholder(dtype=tf.float32, shape=[None])},
+          tf.placeholder(dtype=tf.float32, shape=[None])))
 
   # During predict mode, expect to receive a `tf.Example` proto, so a parsing
   # function is used.
@@ -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(
@@ -93,10 +96,11 @@ class SavedModelEstimator(estimator_lib.Estimator):
 
   # Option 2: Only export predict mode
   export_dir = classifier.export_savedmodel(
-    '/tmp/export_predict', serving_input_receiver_fn)
+      '/tmp/export_predict', serving_input_receiver_fn)
   ```
 
   **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()])}