Update generated Python Op docs.

Change: 144155792

6 files changed, 6 insertions, 6 deletions

diff --git a/tensorflow/g3doc/api_docs/python/constant_op.md b/tensorflow/g3doc/api_docs/python/constant_op.md
index 604edf6660..561e6c8d6c 100644
--- a/tensorflow/g3doc/api_docs/python/constant_op.md
+++ b/tensorflow/g3doc/api_docs/python/constant_op.md
@@ -627,7 +627,7 @@ Example:
 ##### Returns:
 
 
-*  <b>`samples`</b>: a `Tensor` of shape `tf.concat_v2(shape, tf.shape(alpha + beta))`
+*  <b>`samples`</b>: a `Tensor` of shape `tf.concat(shape, tf.shape(alpha + beta))`
     with values of type `dtype`.
 
 
diff --git a/tensorflow/g3doc/api_docs/python/control_flow_ops.md b/tensorflow/g3doc/api_docs/python/control_flow_ops.md
index f384120175..eee5d68409 100644
--- a/tensorflow/g3doc/api_docs/python/control_flow_ops.md
+++ b/tensorflow/g3doc/api_docs/python/control_flow_ops.md
@@ -383,7 +383,7 @@ Example using shape_invariants:
   i0 = tf.constant(0)
   m0 = tf.ones([2, 2])
   c = lambda i, m: i < 10
-  b = lambda i, m: [i+1, tf.concat_v2([m, m], axis=0)]
+  b = lambda i, m: [i+1, tf.concat([m, m], axis=0)]
   tf.while_loop(
       c, b, loop_vars=[i0, m0],
       shape_invariants=[i0.get_shape(), tf.TensorShape([None, 2])])
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.random_gamma.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.random_gamma.md
index d0a15e1503..1d99f8c2f8 100644
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.random_gamma.md
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.random_gamma.md
@@ -60,6 +60,6 @@ Example:
 ##### Returns:
 
 
-*  <b>`samples`</b>: a `Tensor` of shape `tf.concat_v2(shape, tf.shape(alpha + beta))`
+*  <b>`samples`</b>: a `Tensor` of shape `tf.concat(shape, tf.shape(alpha + beta))`
     with values of type `dtype`.
 
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.while_loop.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.while_loop.md
index 3bacda32c9..da39478841 100644
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.while_loop.md
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.while_loop.md
@@ -109,7 +109,7 @@ Example using shape_invariants:
   i0 = tf.constant(0)
   m0 = tf.ones([2, 2])
   c = lambda i, m: i < 10
-  b = lambda i, m: [i+1, tf.concat_v2([m, m], axis=0)]
+  b = lambda i, m: [i+1, tf.concat([m, m], axis=0)]
   tf.while_loop(
       c, b, loop_vars=[i0, m0],
       shape_invariants=[i0.get_shape(), tf.TensorShape([None, 2])])
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.nn.bidirectional_dynamic_rnn.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.nn.bidirectional_dynamic_rnn.md
index 4711118e76..e57bbb03d0 100644
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.nn.bidirectional_dynamic_rnn.md
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.nn.bidirectional_dynamic_rnn.md
@@ -73,7 +73,7 @@ given.
       It returns a tuple instead of a single concatenated `Tensor`, unlike
       in the `bidirectional_rnn`. If the concatenated one is preferred,
       the forward and backward outputs can be concatenated as
-      `tf.concat_v2(outputs, 2)`.
+      `tf.concat(outputs, 2)`.
 *  <b>`output_states`</b>: A tuple (output_state_fw, output_state_bw) containing
       the forward and the backward final states of bidirectional rnn.
 
diff --git a/tensorflow/g3doc/api_docs/python/nn.md b/tensorflow/g3doc/api_docs/python/nn.md
index 983f68f855..84aaa5c5c9 100644
--- a/tensorflow/g3doc/api_docs/python/nn.md
+++ b/tensorflow/g3doc/api_docs/python/nn.md
@@ -2779,7 +2779,7 @@ given.
       It returns a tuple instead of a single concatenated `Tensor`, unlike
       in the `bidirectional_rnn`. If the concatenated one is preferred,
       the forward and backward outputs can be concatenated as
-      `tf.concat_v2(outputs, 2)`.
+      `tf.concat(outputs, 2)`.
 *  <b>`output_states`</b>: A tuple (output_state_fw, output_state_bw) containing
       the forward and the backward final states of bidirectional rnn.