[keras/feature_column/dense_features.py,keras/feature_column/sequence_feature_column.py] Standardise docstring usage of "Default to"

Author: SamuelMarks

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:

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: