resolve line-too-long in initializers

keras/initializers/__init__.py

@@ -67,7 +67,8 @@ def populate_deserializable_objects():
     LOCAL.ALL_OBJECTS["ZerosV2"] = initializers_v2.Zeros
 
     # Out of an abundance of caution we also include these aliases that have
-    # a non-zero probability of having been included in saved configs in the past.
+    # a non-zero probability of having been included in saved configs in the
+    # past.
     LOCAL.ALL_OBJECTS["glorot_normalV2"] = initializers_v2.GlorotNormal
     LOCAL.ALL_OBJECTS["glorot_uniformV2"] = initializers_v2.GlorotUniform
     LOCAL.ALL_OBJECTS["he_normalV2"] = initializers_v2.HeNormal
@@ -150,16 +151,16 @@ def deserialize(config, custom_objects=None):
 def get(identifier):
     """Retrieve a Keras initializer by the identifier.
 
-    The `identifier` may be the string name of a initializers function or class (
-    case-sensitively).
+    The `identifier` may be the string name of a initializers function or class
+    (case-sensitively).
 
     >>> identifier = 'Ones'
     >>> tf.keras.initializers.deserialize(identifier)
     <...keras.initializers.initializers_v2.Ones...>
 
     You can also specify `config` of the initializer to this function by passing
-    dict containing `class_name` and `config` as an identifier. Also note that the
-    `class_name` must map to a `Initializer` class.
+    dict containing `class_name` and `config` as an identifier. Also note that
+    the `class_name` must map to a `Initializer` class.
 
     >>> cfg = {'class_name': 'Ones', 'config': {}}
     >>> tf.keras.initializers.deserialize(cfg)

keras/initializers/initializers_test.py

@@ -65,8 +65,8 @@ def _runner(
         target_max=None,
         target_min=None,
     ):
-        # The global seed is set so that we can get the same random streams between
-        # eager and graph mode when stateful op is used.
+        # The global seed is set so that we can get the same random streams
+        # between eager and graph mode when stateful op is used.
         tf.random.set_seed(1337)
         variable = backend.variable(init(shape))
         output = backend.get_value(variable)
@@ -314,8 +314,9 @@ def test_partition(self, initializer_cls, kwargs):
             self.assertEqual(result.shape, (2, 2))
 
             if hasattr(initializer, "seed"):
-                # Make sure the result are different when the partition_shape is same,
-                # but partition_offset is different, for random related initializers.
+                # Make sure the result are different when the partition_shape is
+                # same, but partition_offset is different, for random related
+                # initializers.
                 result_2 = initializer(
                     shape=(4, 2),
                     partition_shape=(2, 2),
@@ -325,9 +326,11 @@ def test_partition(self, initializer_cls, kwargs):
 
                 # Make sure initializer produce same result when provide same
                 # partition offset.
-                # TODO(scottzhu): Enable this assert when initializer is fully stateless
+                # TODO(scottzhu): Enable this assert when initializer is fully
+                # stateless
                 # result_3 = initializer(
-                #     shape=(4, 2), partition_shape=(2, 2), partition_offset=(1, 0))
+                #     shape=(4, 2), partition_shape=(2, 2), partition_offset=(1,
+                #     0))
                 # self.assertAllClose(result_2, result_3)
 
     @parameterized.named_parameters(

keras/initializers/initializers_v1.py

@@ -71,8 +71,8 @@ class RandomNormal(tf.compat.v1.random_normal_initializer):
     Args:
       mean: a python scalar or a scalar tensor. Mean of the random values to
         generate.
-      stddev: a python scalar or a scalar tensor. Standard deviation of the random
-        values to generate.
+      stddev: a python scalar or a scalar tensor. Standard deviation of the
+        random values to generate.
       seed: A Python integer. Used to create random seeds. See
         `tf.compat.v1.set_random_seed` for behavior.
       dtype: Default data type, used if no `dtype` argument is provided when