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, 8 insertions, 8 deletions

diff --git a/tensorflow/python/feature_column/feature_column.py b/tensorflow/python/feature_column/feature_column.py
index d091d2fe0a..2246d2f3e9 100644
--- a/tensorflow/python/feature_column/feature_column.py
+++ b/tensorflow/python/feature_column/feature_column.py
@@ -16,7 +16,7 @@
 
 FeatureColumns provide a high level abstraction for ingesting and representing
 features. FeatureColumns are also the primary way of encoding features for
-canned @{tf.estimator.Estimator}s.
+canned `tf.estimator.Estimator`s.
 
 When using FeatureColumns with `Estimators`, the type of feature column you
 should choose depends on (1) the feature type and (2) the model type.
@@ -1936,7 +1936,7 @@ class _FeatureColumn(object):
 
     It is used for get_parsing_spec for `tf.parse_example`. Returned spec is a
     dict from keys ('string') to `VarLenFeature`, `FixedLenFeature`, and other
-    supported objects. Please check documentation of @{tf.parse_example} for all
+    supported objects. Please check documentation of `tf.parse_example` for all
     supported spec objects.
 
     Let's say a Feature column depends on raw feature ('raw') and another
@@ -1995,7 +1995,7 @@ class _DenseColumn(_FeatureColumn):
       weight_collections: List of graph collections to which Variables (if any
         will be created) are added.
       trainable: If `True` also add variables to the graph collection
-        `GraphKeys.TRAINABLE_VARIABLES` (see @{tf.Variable}).
+        `GraphKeys.TRAINABLE_VARIABLES` (see `tf.Variable`).
 
     Returns:
       `Tensor` of shape [batch_size] + `_variable_shape`.
@@ -2062,7 +2062,7 @@ class _CategoricalColumn(_FeatureColumn):
   WARNING: Do not subclass this layer unless you know what you are doing:
   the API is subject to future changes.
 
-  A categorical feature typically handled with a @{tf.SparseTensor} of IDs.
+  A categorical feature typically handled with a `tf.SparseTensor` of IDs.
   """
   __metaclass__ = abc.ABCMeta
 
@@ -2097,7 +2097,7 @@ class _CategoricalColumn(_FeatureColumn):
       weight_collections: List of graph collections to which variables (if any
         will be created) are added.
       trainable: If `True` also add variables to the graph collection
-        `GraphKeys.TRAINABLE_VARIABLES` (see @{tf.get_variable}).
+        `GraphKeys.TRAINABLE_VARIABLES` (see `tf.get_variable`).
     """
     pass
 
diff --git a/tensorflow/python/feature_column/feature_column_v2.py b/tensorflow/python/feature_column/feature_column_v2.py
index b4dd23f58d..b6bf516286 100644
--- a/tensorflow/python/feature_column/feature_column_v2.py
+++ b/tensorflow/python/feature_column/feature_column_v2.py
@@ -16,7 +16,7 @@
 
 FeatureColumns provide a high level abstraction for ingesting and representing
 features. FeatureColumns are also the primary way of encoding features for
-canned @{tf.estimator.Estimator}s.
+canned `tf.estimator.Estimator`s.
 
 When using FeatureColumns with `Estimators`, the type of feature column you
 should choose depends on (1) the feature type and (2) the model type.
@@ -1904,7 +1904,7 @@ class FeatureColumn(object):
 
     It is used for get_parsing_spec for `tf.parse_example`. Returned spec is a
     dict from keys ('string') to `VarLenFeature`, `FixedLenFeature`, and other
-    supported objects. Please check documentation of @{tf.parse_example} for all
+    supported objects. Please check documentation of `tf.parse_example` for all
     supported spec objects.
 
     Let's say a Feature column depends on raw feature ('raw') and another
@@ -2025,7 +2025,7 @@ def _create_dense_column_weighted_sum(column,
 class CategoricalColumn(FeatureColumn):
   """Represents a categorical feature.
 
-  A categorical feature typically handled with a @{tf.SparseTensor} of IDs.
+  A categorical feature typically handled with a `tf.SparseTensor` of IDs.
   """
   __metaclass__ = abc.ABCMeta