Fix documentation compatibility tag formatting.

PiperOrigin-RevId: 310461418
Change-Id: I824ac86b0519d7429bd68189fbf088c30484a681

tensorflow/python/framework/ops.py

@@ -6261,10 +6261,12 @@ def add_to_collection(name, value):
   Args:
     name: The key for the collection. For example, the `GraphKeys` class
       contains many standard names for collections.
-    value: The value to add to the collection.  @compatibility(eager)
-      Collections are only supported in eager when variables are created inside
-      an EagerVariableStore (e.g. as part of a layer or template).
-      @end_compatibility
+    value: The value to add to the collection.
+
+  @compatibility(eager)
+  Collections are only supported in eager when variables are created inside
+  an EagerVariableStore (e.g. as part of a layer or template).
+  @end_compatibility
   """
   get_default_graph().add_to_collection(name, value)
 
@@ -6279,10 +6281,12 @@ def add_to_collections(names, value):
   Args:
     names: The key for the collections. The `GraphKeys` class contains many
       standard names for collections.
-    value: The value to add to the collections.  @compatibility(eager)
-      Collections are only supported in eager when variables are created inside
-      an EagerVariableStore (e.g. as part of a layer or template).
-      @end_compatibility
+    value: The value to add to the collections.
+
+  @compatibility(eager)
+  Collections are only supported in eager when variables are created inside
+  an EagerVariableStore (e.g. as part of a layer or template).
+  @end_compatibility
   """
   get_default_graph().add_to_collections(names, value)
 

tensorflow/python/keras/optimizer_v2/rmsprop.py

@@ -121,16 +121,19 @@ class RMSprop(optimizer_v2.OptimizerV2):
         Setting this to `True` may help with training, but is slightly more
         expensive in terms of computation and memory. Defaults to `False`.
       name: Optional name prefix for the operations created when applying
-        gradients. Defaults to "RMSprop".  @compatibility(eager) When eager
-        execution is enabled, `learning_rate`, `decay`, `momentum`, and
-        `epsilon` can each be a callable that takes no arguments and returns the
-        actual value to use. This can be useful for changing these values across
-        different invocations of optimizer functions. @end_compatibility
+        gradients. Defaults to "RMSprop".
       **kwargs: keyword arguments. Allowed to be {`clipnorm`, `clipvalue`, `lr`,
         `decay`}. `clipnorm` is clip gradients by norm; `clipvalue` is clip
         gradients by value, `decay` is included for backward compatibility to
         allow time inverse decay of learning rate. `lr` is included for backward
         compatibility, recommended to use `learning_rate` instead.
+
+    @compatibility(eager)
+    When eager execution is enabled, `learning_rate`, `decay`, `momentum`, and
+    `epsilon` can each be a callable that takes no arguments and returns the
+    actual value to use. This can be useful for changing these values across
+    different invocations of optimizer functions.
+    @end_compatibility
     """
     super(RMSprop, self).__init__(name, **kwargs)
     self._set_hyper("learning_rate", kwargs.get("lr", learning_rate))

tensorflow/python/training/adam.py

@@ -92,11 +92,14 @@ class AdamOptimizer(optimizer.Optimizer):
         Section 2.1), not the epsilon in Algorithm 1 of the paper.
       use_locking: If True use locks for update operations.
       name: Optional name for the operations created when applying gradients.
-        Defaults to "Adam".  @compatibility(eager) When eager execution is
-        enabled, `learning_rate`, `beta1`, `beta2`, and `epsilon` can each be a
-        callable that takes no arguments and returns the actual value to use.
-        This can be useful for changing these values across different
-        invocations of optimizer functions. @end_compatibility
+        Defaults to "Adam".
+
+    @compatibility(eager)
+    When eager execution is enabled, `learning_rate`, `beta1`, `beta2`, and
+    `epsilon` can each be a callable that takes no arguments and returns the
+    actual value to use. This can be useful for changing these values across
+    different invocations of optimizer functions.
+    @end_compatibility
     """
     super(AdamOptimizer, self).__init__(use_locking, name)
     self._lr = learning_rate