Remove usage of magic-api-link syntax from source files.

Back-ticks are now converted to links in the api_docs generator. With the new docs repo we're moving to simplify the docs pipeline, and make everything more readable.

By doing this we no longer get test failures for symbols that don't exist (`tf.does_not_exist`  will not get a link).

There is also no way, not to set custom link text. That's okay.

This is the result of the following regex replacement (+ a couple of manual edits.):

re: @\{([^$].*?)(\$.+?)?}
sub: `\1`

Which does the following replacements:

"@{tf.symbol}" --> "`tf.symbol`"
"@{tf.symbol$link_text}" --> "`tf.symbol`"

PiperOrigin-RevId: 208042358

2 files changed, 17 insertions, 17 deletions

diff --git a/tensorflow/contrib/cudnn_rnn/python/layers/cudnn_rnn.py b/tensorflow/contrib/cudnn_rnn/python/layers/cudnn_rnn.py
index d58198faf3..e26d56c857 100644
--- a/tensorflow/contrib/cudnn_rnn/python/layers/cudnn_rnn.py
+++ b/tensorflow/contrib/cudnn_rnn/python/layers/cudnn_rnn.py
@@ -56,7 +56,7 @@ class _CudnnRNN(base_layer.Layer):
   Cudnn RNNs have two major differences from other platform-independent RNNs tf
   provides:
   * Cudnn LSTM and GRU are mathematically different from their tf counterparts.
-    (e.g. @{tf.contrib.rnn.LSTMBlockCell} and @{tf.nn.rnn_cell.GRUCell}.
+    (e.g. `tf.contrib.rnn.LSTMBlockCell` and `tf.nn.rnn_cell.GRUCell`.
   * Cudnn-trained checkpoints are not directly compatible with tf RNNs:
     * They use a single opaque parameter buffer for the entire (possibly)
       multi-layer multi-directional RNN; Whereas tf RNN weights are per-cell and
@@ -182,7 +182,7 @@ class _CudnnRNN(base_layer.Layer):
       dropout: dropout rate, a number between [0, 1]. Dropout is applied between
           each layer (no dropout is applied for a model with a single layer).
           When set to 0, dropout is disabled.
-      seed: the op seed used for initializing dropout. See @{tf.set_random_seed}
+      seed: the op seed used for initializing dropout. See `tf.set_random_seed`
           for behavior.
       dtype: tf.float16, tf.float32 or tf.float64
       kernel_initializer: starting value to initialize the weight.
diff --git a/tensorflow/contrib/cudnn_rnn/python/ops/cudnn_rnn_ops.py b/tensorflow/contrib/cudnn_rnn/python/ops/cudnn_rnn_ops.py
index 748d7cd011..2c92f31788 100644
--- a/tensorflow/contrib/cudnn_rnn/python/ops/cudnn_rnn_ops.py
+++ b/tensorflow/contrib/cudnn_rnn/python/ops/cudnn_rnn_ops.py
@@ -61,8 +61,8 @@ _WEIGHTS_VARIABLE_NAME = rnn_cell_impl._WEIGHTS_VARIABLE_NAME
 class CudnnCompatibleLSTMCell(lstm_ops.LSTMBlockCell):
   """Cudnn Compatible LSTMCell.
 
-  A simple wrapper around @{tf.contrib.rnn.LSTMBlockCell} to use along with
-  @{tf.contrib.cudnn_rnn.CudnnLSTM}. The latter's params can be used by
+  A simple wrapper around `tf.contrib.rnn.LSTMBlockCell` to use along with
+  `tf.contrib.cudnn_rnn.CudnnLSTM`. The latter's params can be used by
   this cell seamlessly.
   """
 
@@ -76,8 +76,8 @@ class CudnnCompatibleLSTMCell(lstm_ops.LSTMBlockCell):
 class CudnnCompatibleGRUCell(rnn_cell_impl.GRUCell):
   """Cudnn Compatible GRUCell.
 
-  A GRU impl akin to @{tf.nn.rnn_cell.GRUCell} to use along with
-  @{tf.contrib.cudnn_rnn.CudnnGRU}. The latter's params can be used by
+  A GRU impl akin to `tf.nn.rnn_cell.GRUCell` to use along with
+  `tf.contrib.cudnn_rnn.CudnnGRU`. The latter's params can be used by
   it seamlessly.
 
   It differs from platform-independent GRUs in how the new memory gate is
@@ -97,7 +97,7 @@ class CudnnCompatibleGRUCell(rnn_cell_impl.GRUCell):
   $$h_t = (1 - u_t) .* h'_t + u_t .* h_t-1$$
   ```
 
-  Other GRU (see @{tf.nn.rnn_cell.GRUCell} and @{tf.contrib.rnn.GRUBlockCell}):
+  Other GRU (see `tf.nn.rnn_cell.GRUCell` and `tf.contrib.rnn.GRUBlockCell`):
   ```python
   # new memory gate
   \\(h'_t = tanh(x_t * W_h + (r_t .* h_t-1) * R_h + b_{Wh})\\)
@@ -891,7 +891,7 @@ def _cudnn_rnn(inputs,
     direction: the direction model that the model operates. Could be either
         'unidirectional' or 'bidirectional'
     dropout: whether to enable dropout. With it is 0, dropout is disabled.
-    seed: the op seed used for initializing dropout. See @{tf.set_random_seed}
+    seed: the op seed used for initializing dropout. See `tf.set_random_seed`
         for behavior.
     name: name of the operation.
   Returns:
@@ -957,7 +957,7 @@ def cudnn_lstm(inputs,
     direction: the direction model that the model operates. Could be either
         'unidirectional' or 'bidirectional'
     dropout: whether to enable dropout. With it is 0, dropout is disabled.
-    seed: the op seed used for initializing dropout. See @{tf.set_random_seed}
+    seed: the op seed used for initializing dropout. See `tf.set_random_seed`
         for behavior.
     name: name of the operation.
   Returns:
@@ -998,7 +998,7 @@ def _cudnn_rnn_no_input_c(inputs,
     direction: the direction model that the model operates. Could be either
         'unidirectional' or 'bidirectional'
     dropout: whether to enable dropout. With it is 0, dropout is disabled.
-    seed: the op seed used for initializing dropout. See @{tf.set_random_seed}
+    seed: the op seed used for initializing dropout. See `tf.set_random_seed`
         for behavior.
     name: name of the operation.
   Returns:
@@ -1040,7 +1040,7 @@ def cudnn_gru(inputs,
     direction: the direction model that the model operates. Could be either
         'unidirectional' or 'bidirectional'
     dropout: whether to enable dropout. With it is 0, dropout is disabled.
-    seed: the op seed used for initializing dropout. See @{tf.set_random_seed}
+    seed: the op seed used for initializing dropout. See `tf.set_random_seed`
         for behavior.
     name: name of the operation.
   Returns:
@@ -1079,7 +1079,7 @@ def cudnn_rnn_relu(inputs,
     direction: the direction model that the model operates. Could be either
         'unidirectional' or 'bidirectional'
     dropout: whether to enable dropout. With it is 0, dropout is disabled.
-    seed: the op seed used for initializing dropout. See @{tf.set_random_seed}
+    seed: the op seed used for initializing dropout. See `tf.set_random_seed`
         for behavior.
     name: name of the operation.
   Returns:
@@ -1119,7 +1119,7 @@ def cudnn_rnn_tanh(inputs,
     direction: the direction model that the model operates. Could be either
         'unidirectional' or 'bidirectional'
     dropout: whether to enable dropout. With it is 0, dropout is disabled.
-    seed: the op seed used for initializing dropout. See @{tf.set_random_seed}
+    seed: the op seed used for initializing dropout. See `tf.set_random_seed`
         for behavior.
     name: name of the operation.
   Returns:
@@ -1161,7 +1161,7 @@ def cudnn_rnn_opaque_params_to_canonical(rnn_mode,
     direction: the direction model that the model operates. Could be either
         'unidirectional' or 'bidirectional'
     dropout: whether to enable dropout. With it is 0, dropout is disabled.
-    seed: the op seed used for initializing dropout. See @{tf.set_random_seed}
+    seed: the op seed used for initializing dropout. See `tf.set_random_seed`
         for behavior.
     name: name of the operation.
   Returns:
@@ -1224,7 +1224,7 @@ def cudnn_rnn_canonical_to_opaque_params(rnn_mode,
     direction: the direction model that the model operates. Could be either
         'unidirectional' or 'bidirectional'
     dropout: whether to enable dropout. With it is 0, dropout is disabled.
-    seed: the op seed used for initializing dropout. See @{tf.set_random_seed}
+    seed: the op seed used for initializing dropout. See `tf.set_random_seed`
         for behavior.
     name: name of the operation.
   Returns:
@@ -1282,7 +1282,7 @@ def cudnn_rnn_opaque_params_size(rnn_mode,
         'unidirectional' or 'bidirectional'
     dtype: one of tf.float32 or tf.float64.
     dropout: whether to enable dropout. With it is 0, dropout is disabled.
-    seed: the op seed used for initializing dropout. See @{tf.set_random_seed}
+    seed: the op seed used for initializing dropout. See `tf.set_random_seed`
         for behavior.
     name: name of the operation.
   Returns:
@@ -1349,7 +1349,7 @@ class _CudnnRNN(object):
           'unidirectional' or 'bidirectional'
       dtype: dtype of params, tf.float32 or tf.float64.
       dropout: whether to enable dropout. With it is 0, dropout is disabled.
-      seed: the op seed used for initializing dropout. See @{tf.set_random_seed}
+      seed: the op seed used for initializing dropout. See `tf.set_random_seed`
           for behavior.
     Raises:
       ValueError: if direction is invalid.