Merge pull request #24173 from vidakDK:master

PiperOrigin-RevId: 224576050

tensorflow/python/training/saver.py

@@ -1377,6 +1377,37 @@ def import_meta_graph(meta_graph_or_file, clear_devices=False,
   NOTE: Restarting training from saved `meta_graph` only works if the
   device assignments have not changed.
 
+  Example 2:
+  Variables, placeholders, and independent operations can also be stored, as
+  shown in the following example.
+
+  ```Python
+  # Saving contents and operations.
+  v1 = tf.placeholder(tf.float32, name="v1")
+  v2 = tf.placeholder(tf.float32, name="v2")
+  v3 = tf.mul(v1, v2)
+  vx = tf.Variable(10.0, name="vx")
+  v4 = tf.add(v3, vx, name="v4")
+  saver = tf.train.Saver([vx])
+  sess = tf.Session()
+  sess.run(tf.initialize_all_variables())
+  sess.run(vx.assign(tf.add(vx, vx)))
+  result = sess.run(v4, feed_dict={v1:12.0, v2:3.3})
+  print(result)
+  saver.save(sess, "./model_ex1")
+  ```
+
+  Later this model can be restored and contents loaded.
+
+  ```Python
+  # Restoring variables and running operations.
+  saver = tf.train.import_meta_graph("./model_ex1.meta")
+  sess = tf.Session()
+  saver.restore(sess, "./model_ex1")
+  result = sess.run("v4:0", feed_dict={"v1:0": 12.0, "v2:0": 3.3})
+  print(result)
+  ```
+
   Args:
     meta_graph_or_file: `MetaGraphDef` protocol buffer or filename (including
       the path) containing a `MetaGraphDef`.