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diff --git a/tensorflow/python/framework/random_seed.py b/tensorflow/python/framework/random_seed.py
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+"""For seeding individual ops based on a graph-level seed.
+"""
+
+from tensorflow.python.framework import ops
+
+
+_DEFAULT_GRAPH_SEED = 87654321
+
+
+def get_seed(op_seed):
+  """Returns the local seeds an operation should use given an op-specific seed.
+
+  Given operation-specific seed, `op_seed`, this helper function returns two
+  seeds derived from graph-level and op-level seeds. Many random operations
+  internally use the two seeds to allow user to change the seed globally for a
+  graph, or for only specific operations.
+
+  For details on how the graph-level seed interacts with op seeds, see
+  [`set_random_seed`](constant_op.md#set_random_seed).
+
+  Args:
+    op_seed: integer.
+
+  Returns:
+    A tuple of two integers that should be used for the local seed of this
+    operation.
+  """
+  graph_seed = ops.get_default_graph().seed
+  if graph_seed is not None:
+    if op_seed is not None:
+      return graph_seed, op_seed
+    else:
+      return graph_seed, ops.get_default_graph()._last_id
+  else:
+    if op_seed is not None:
+      return _DEFAULT_GRAPH_SEED, op_seed
+    else:
+      return None, None
+
+
+def set_random_seed(seed):
+  """Sets the graph-level random seed.
+
+  Operations that rely on a random seed actually derive it from two seeds:
+  the graph-level and operation-level seeds. This sets the graph-level seed.
+
+  Its interactions with operation-level seeds is as follows:
+
+    1. If neither the graph-level nor the operation seed is set:
+      A random seed is used for this op.
+    2. If the graph-level seed is set, but the operation seed is not:
+      The system deterministically picks an operation seed in conjunction
+      with the graph-level seed so that it gets a unique random sequence.
+    3. If the graph-level seed is not set, but the operation seed is set:
+      A default graph-level seed and the specified operation seed are used to
+      determine the random sequence.
+    4. If both the graph-level and the operation seed are set:
+      Both seeds are used in conjunction to determine the random sequence.
+
+  To illustrate the user-visible effects, consider these examples:
+
+  To generate different sequences across sessions, set neither
+  graph-level nor op-level seeds:
+
+  ```python
+  a = tf.random_uniform([1])
+  b = tf.random_normal([1])
+
+  print "Session 1"
+  with tf.Session() as sess1:
+    print sess1.run(a)  # generates 'A1'
+    print sess1.run(a)  # generates 'A2'
+    print sess1.run(b)  # generates 'B1'
+    print sess1.run(b)  # generates 'B2'
+
+  print "Session 2"
+  with tf.Session() as sess2:
+    print sess2.run(a)  # generates 'A3'
+    print sess2.run(a)  # generates 'A4'
+    print sess2.run(b)  # generates 'B3'
+    print sess2.run(b)  # generates 'B4'
+  ```
+
+  To generate the same repeatable sequence for an op across sessions, set the
+  seed for the op:
+
+  ```python
+  a = tf.random_uniform([1], seed=1)
+  b = tf.random_normal([1])
+
+  # Repeatedly running this block with the same graph will generate the same
+  # sequence of values for 'a', but different sequences of values for 'b'.
+  print "Session 1"
+  with tf.Session() as sess1:
+    print sess1.run(a)  # generates 'A1'
+    print sess1.run(a)  # generates 'A2'
+    print sess1.run(b)  # generates 'B1'
+    print sess1.run(b)  # generates 'B2'
+
+  print "Session 2"
+  with tf.Session() as sess2:
+    print sess2.run(a)  # generates 'A1'
+    print sess2.run(a)  # generates 'A2'
+    print sess2.run(b)  # generates 'B3'
+    print sess2.run(b)  # generates 'B4'
+  ```
+
+  To make the random sequences generated by all ops be repeatable across
+  sessions, set a graph-level seed:
+
+  ```python
+  tf.set_random_seed(1234)
+  a = tf.random_uniform([1])
+  b = tf.random_normal([1])
+
+  # Repeatedly running this block with the same graph will generate different
+  # sequences of 'a' and 'b'.
+  print "Session 1"
+  with tf.Session() as sess1:
+    print sess1.run(a)  # generates 'A1'
+    print sess1.run(a)  # generates 'A2'
+    print sess1.run(b)  # generates 'B1'
+    print sess1.run(b)  # generates 'B2'
+
+  print "Session 2"
+  with tf.Session() as sess2:
+    print sess2.run(a)  # generates 'A1'
+    print sess2.run(a)  # generates 'A2'
+    print sess2.run(b)  # generates 'B1'
+    print sess2.run(b)  # generates 'B2'
+  ```
+
+  Args:
+    seed: integer.
+  """
+  ops.get_default_graph().seed = seed