From c7157c028d258751a195e6f7ffc6e0360509b4de Mon Sep 17 00:00:00 2001
From: Samuel Marks <807580+SamuelMarks@users.noreply.github.com>
Date: Wed, 12 Apr 2023 20:36:23 -0400
Subject: [PATCH] 
 [keras/feature_column/dense_features.py,keras/feature_column/sequence_feature_column.py]
 Standardise docstring usage of "Default to"

---
 keras/feature_column/dense_features.py          | 6 +++---
 keras/feature_column/sequence_feature_column.py | 4 ++--
 2 files changed, 5 insertions(+), 5 deletions(-)

diff --git a/keras/feature_column/dense_features.py b/keras/feature_column/dense_features.py
index fb8c801e65c5..f5ae664581cc 100644
--- a/keras/feature_column/dense_features.py
+++ b/keras/feature_column/dense_features.py
@@ -90,7 +90,7 @@ def __init__(
           trainable:  Boolean, whether the layer's variables will be updated via
             gradient descent during training.
           name: Name to give to the DenseFeatures.
-          partitioner: Partitioner for input layer. Defaults to None.
+          partitioner: Partitioner for input layer. Defaults to `None`.
           **kwargs: Keyword arguments to construct a layer.
 
         Raises:
@@ -150,8 +150,8 @@ def call(self, features, cols_to_output_tensors=None, training=None):
             method of any `FeatureColumn` that takes a `training` argument. For
             example, if a `FeatureColumn` performed dropout, the column could
             expose a `training` argument to control whether the dropout should
-            be applied. If `None`, defaults to
-            `tf.keras.backend.learning_phase()`.
+            be applied. If `None`, becomes `tf.keras.backend.learning_phase()`.
+            Defaults to `None`.
 
 
         Returns:
diff --git a/keras/feature_column/sequence_feature_column.py b/keras/feature_column/sequence_feature_column.py
index 5fd05fdd6656..89e4f5cfdb76 100644
--- a/keras/feature_column/sequence_feature_column.py
+++ b/keras/feature_column/sequence_feature_column.py
@@ -122,8 +122,8 @@ def call(self, features, training=None):
             method of any `FeatureColumn` that takes a `training` argument. For
             example, if a `FeatureColumn` performed dropout, the column could
             expose a `training` argument to control whether the dropout should
-            be applied. If `None`, defaults to
-            `tf.keras.backend.learning_phase()`.
+            be applied. If `None`, becomes `tf.keras.backend.learning_phase()`.
+            Defaults to `None`.
 
 
         Returns: