Update generated Python Op docs.

Change: 130061676

tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.nn.rnn_cell.LSTMCell.md

@@ -19,7 +19,7 @@ The class uses optional peep-hole connections, optional cell clipping, and
 an optional projection layer.
 - - -
 
-#### `tf.nn.rnn_cell.LSTMCell.__init__(num_units, input_size=None, use_peepholes=False, cell_clip=None, initializer=None, num_proj=None, proj_clip=None, num_unit_shards=1, num_proj_shards=1, forget_bias=1.0, state_is_tuple=False, activation=tanh)` {#LSTMCell.__init__}
+#### `tf.nn.rnn_cell.LSTMCell.__init__(num_units, input_size=None, use_peepholes=False, cell_clip=None, initializer=None, num_proj=None, proj_clip=None, num_unit_shards=1, num_proj_shards=1, forget_bias=1.0, state_is_tuple=True, activation=tanh)` {#LSTMCell.__init__}
 
 Initialize the parameters for an LSTM cell.
 
@@ -47,8 +47,8 @@ Initialize the parameters for an LSTM cell.
     in order to reduce the scale of forgetting at the beginning of
     the training.
 *  <b>`state_is_tuple`</b>: If True, accepted and returned states are 2-tuples of
-    the `c_state` and `m_state`.  By default (False), they are concatenated
-    along the column axis.  This default behavior will soon be deprecated.
+    the `c_state` and `m_state`.  If False, they are concatenated
+    along the column axis.  This latter behavior will soon be deprecated.
 *  <b>`activation`</b>: Activation function of the inner states.
 
 

tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.nn.rnn_cell.BasicLSTMCell.md

@@ -11,7 +11,7 @@ use peep-hole connections: it is the basic baseline.
 For advanced models, please use the full LSTMCell that follows.
 - - -
 
-#### `tf.nn.rnn_cell.BasicLSTMCell.__init__(num_units, forget_bias=1.0, input_size=None, state_is_tuple=False, activation=tanh)` {#BasicLSTMCell.__init__}
+#### `tf.nn.rnn_cell.BasicLSTMCell.__init__(num_units, forget_bias=1.0, input_size=None, state_is_tuple=True, activation=tanh)` {#BasicLSTMCell.__init__}
 
 Initialize the basic LSTM cell.
 
@@ -22,8 +22,8 @@ Initialize the basic LSTM cell.
 *  <b>`forget_bias`</b>: float, The bias added to forget gates (see above).
 *  <b>`input_size`</b>: Deprecated and unused.
 *  <b>`state_is_tuple`</b>: If True, accepted and returned states are 2-tuples of
-    the `c_state` and `m_state`.  By default (False), they are concatenated
-    along the column axis.  This default behavior will soon be deprecated.
+    the `c_state` and `m_state`.  If False, they are concatenated
+    along the column axis.  The latter behavior will soon be deprecated.
 *  <b>`activation`</b>: Activation function of the inner states.
 
 

tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.nn.rnn_cell.MultiRNNCell.md

@@ -1,7 +1,7 @@
 RNN cell composed sequentially of multiple simple cells.
 - - -
 
-#### `tf.nn.rnn_cell.MultiRNNCell.__init__(cells, state_is_tuple=False)` {#MultiRNNCell.__init__}
+#### `tf.nn.rnn_cell.MultiRNNCell.__init__(cells, state_is_tuple=True)` {#MultiRNNCell.__init__}
 
 Create a RNN cell composed sequentially of a number of RNNCells.
 
@@ -10,8 +10,9 @@ Create a RNN cell composed sequentially of a number of RNNCells.
 
 *  <b>`cells`</b>: list of RNNCells that will be composed in this order.
 *  <b>`state_is_tuple`</b>: If True, accepted and returned states are n-tuples, where
-    `n = len(cells)`.  By default (False), the states are all
-    concatenated along the column axis.
+    `n = len(cells)`.  If False, the states are all
+    concatenated along the column axis.  This latter behavior will soon be
+    deprecated.
 
 ##### Raises:
 

tensorflow/g3doc/api_docs/python/rnn_cell.md

@@ -141,7 +141,7 @@ use peep-hole connections: it is the basic baseline.
 For advanced models, please use the full LSTMCell that follows.
 - - -
 
-#### `tf.nn.rnn_cell.BasicLSTMCell.__init__(num_units, forget_bias=1.0, input_size=None, state_is_tuple=False, activation=tanh)` {#BasicLSTMCell.__init__}
+#### `tf.nn.rnn_cell.BasicLSTMCell.__init__(num_units, forget_bias=1.0, input_size=None, state_is_tuple=True, activation=tanh)` {#BasicLSTMCell.__init__}
 
 Initialize the basic LSTM cell.
 
@@ -152,8 +152,8 @@ Initialize the basic LSTM cell.
 *  <b>`forget_bias`</b>: float, The bias added to forget gates (see above).
 *  <b>`input_size`</b>: Deprecated and unused.
 *  <b>`state_is_tuple`</b>: If True, accepted and returned states are 2-tuples of
-    the `c_state` and `m_state`.  By default (False), they are concatenated
-    along the column axis.  This default behavior will soon be deprecated.
+    the `c_state` and `m_state`.  If False, they are concatenated
+    along the column axis.  The latter behavior will soon be deprecated.
 *  <b>`activation`</b>: Activation function of the inner states.
 
 
@@ -268,7 +268,7 @@ The class uses optional peep-hole connections, optional cell clipping, and
 an optional projection layer.
 - - -
 
-#### `tf.nn.rnn_cell.LSTMCell.__init__(num_units, input_size=None, use_peepholes=False, cell_clip=None, initializer=None, num_proj=None, proj_clip=None, num_unit_shards=1, num_proj_shards=1, forget_bias=1.0, state_is_tuple=False, activation=tanh)` {#LSTMCell.__init__}
+#### `tf.nn.rnn_cell.LSTMCell.__init__(num_units, input_size=None, use_peepholes=False, cell_clip=None, initializer=None, num_proj=None, proj_clip=None, num_unit_shards=1, num_proj_shards=1, forget_bias=1.0, state_is_tuple=True, activation=tanh)` {#LSTMCell.__init__}
 
 Initialize the parameters for an LSTM cell.
 
@@ -296,8 +296,8 @@ Initialize the parameters for an LSTM cell.
     in order to reduce the scale of forgetting at the beginning of
     the training.
 *  <b>`state_is_tuple`</b>: If True, accepted and returned states are 2-tuples of
-    the `c_state` and `m_state`.  By default (False), they are concatenated
-    along the column axis.  This default behavior will soon be deprecated.
+    the `c_state` and `m_state`.  If False, they are concatenated
+    along the column axis.  This latter behavior will soon be deprecated.
 *  <b>`activation`</b>: Activation function of the inner states.
 
 
@@ -382,7 +382,7 @@ Alias for field number 1
 RNN cell composed sequentially of multiple simple cells.
 - - -
 
-#### `tf.nn.rnn_cell.MultiRNNCell.__init__(cells, state_is_tuple=False)` {#MultiRNNCell.__init__}
+#### `tf.nn.rnn_cell.MultiRNNCell.__init__(cells, state_is_tuple=True)` {#MultiRNNCell.__init__}
 
 Create a RNN cell composed sequentially of a number of RNNCells.
 
@@ -391,8 +391,9 @@ Create a RNN cell composed sequentially of a number of RNNCells.
 
 *  <b>`cells`</b>: list of RNNCells that will be composed in this order.
 *  <b>`state_is_tuple`</b>: If True, accepted and returned states are n-tuples, where
-    `n = len(cells)`.  By default (False), the states are all
-    concatenated along the column axis.
+    `n = len(cells)`.  If False, the states are all
+    concatenated along the column axis.  This latter behavior will soon be
+    deprecated.
 
 ##### Raises: