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

1 files changed, 8 insertions, 8 deletions

diff --git a/tensorflow/contrib/factorization/python/ops/kmeans.py b/tensorflow/contrib/factorization/python/ops/kmeans.py
index 9ffdd3ba5e..4d8d5004fe 100644
--- a/tensorflow/contrib/factorization/python/ops/kmeans.py
+++ b/tensorflow/contrib/factorization/python/ops/kmeans.py
@@ -158,12 +158,12 @@ class _ModelFn(object):
     return either `features` or, equivalently, `(features, None)`.
 
     Args:
-      features: The input points. See @{tf.estimator.Estimator}.
-      mode: See @{tf.estimator.Estimator}.
-      config: See @{tf.estimator.Estimator}.
+      features: The input points. See `tf.estimator.Estimator`.
+      mode: See `tf.estimator.Estimator`.
+      config: See `tf.estimator.Estimator`.
 
     Returns:
-      A @{tf.estimator.EstimatorSpec} (see @{tf.estimator.Estimator}) specifying
+      A `tf.estimator.EstimatorSpec` (see `tf.estimator.Estimator`) specifying
       this behavior:
         * `train_op`: Execute one mini-batch or full-batch run of Lloyd's
              algorithm.
@@ -394,7 +394,7 @@ class KMeansClustering(estimator.Estimator):
       relative_tolerance: A relative tolerance of change in the loss between
         iterations. Stops learning if the loss changes less than this amount.
         This may not work correctly if `use_mini_batch=True`.
-      config: See @{tf.estimator.Estimator}.
+      config: See `tf.estimator.Estimator`.
       feature_columns: An optionable iterable containing all the feature columns
         used by the model. All items in the set should be feature column
         instances that can be passed to `tf.feature_column.input_layer`. If this
@@ -431,7 +431,7 @@ class KMeansClustering(estimator.Estimator):
     """Finds the index of the closest cluster center to each input point.
 
     Args:
-      input_fn: Input points. See @{tf.estimator.Estimator.predict}.
+      input_fn: Input points. See `tf.estimator.Estimator.predict`.
 
     Yields:
       The index of the closest cluster center for each input point.
@@ -447,7 +447,7 @@ class KMeansClustering(estimator.Estimator):
     which returns the negative sum.
 
     Args:
-      input_fn: Input points. See @{tf.estimator.Estimator.evaluate}. Only one
+      input_fn: Input points. See `tf.estimator.Estimator.evaluate`. Only one
           batch is retrieved.
 
     Returns:
@@ -465,7 +465,7 @@ class KMeansClustering(estimator.Estimator):
     sklearn function returns the Euclidean distance.
 
     Args:
-      input_fn: Input points. See @{tf.estimator.Estimator.predict}.
+      input_fn: Input points. See `tf.estimator.Estimator.predict`.
 
     Yields:
       The distances from each input point to each cluster center.