Cleanup the no-at-top imports in Keras.

Give some explanation for certain case about why it is not avoidable.

PiperOrigin-RevId: 277118837
Change-Id: I9b16ee1ce89b26cf0b07ae4e3eb2af337b11dd9c

tensorflow/python/keras/backend.py

@@ -73,6 +73,7 @@ from tensorflow.python.ops import variables as variables_module
 from tensorflow.python.ops.ragged import ragged_concat_ops
 from tensorflow.python.ops.ragged import ragged_tensor
 from tensorflow.python.platform import tf_logging as logging
+from tensorflow.python.training import moving_averages
 from tensorflow.python.util import nest
 from tensorflow.python.util import object_identity
 from tensorflow.python.util import tf_contextlib
@@ -1601,11 +1602,6 @@ def moving_average_update(x, value, momentum):
   Returns:
       An Operation to update the variable.
   """
-  # `training` is higher-up than the Keras backend in the abstraction hierarchy.
-  # In particular, `training` depends on layers, and thus on Keras.
-  # moving_averages, being low-level ops, should not be part of the training
-  # module.
-  from tensorflow.python.training import moving_averages  # pylint: disable=g-import-not-at-top
   zero_debias = not tf2.enabled()
   return moving_averages.assign_moving_average(
       x, value, momentum, zero_debias=zero_debias)

tensorflow/python/keras/engine/base_layer_utils.py

@@ -41,7 +41,8 @@ _call_context = threading.local()
 
 
 def create_mean_metric(value, name=None):
-  # TODO(psv): Remove this import when b/110718070 is fixed.
+  # import keras will import base_layer and then this module, and metric relies
+  # on base_layer, which result into a cyclic dependency.
   from tensorflow.python.keras import metrics as metrics_module  # pylint: disable=g-import-not-at-top
   metric_obj = metrics_module.Mean(name=name)
   return metric_obj, metric_obj(value)

tensorflow/python/keras/mixed_precision/experimental/__init__.py

@@ -1,23 +0,0 @@
-# Copyright 2019 The TensorFlow Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-"""Mixed precision API."""
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-from tensorflow.python.keras.mixed_precision.experimental.loss_scale_optimizer import LossScaleOptimizer
-from tensorflow.python.keras.mixed_precision.experimental.policy import global_policy
-from tensorflow.python.keras.mixed_precision.experimental.policy import Policy
-from tensorflow.python.keras.mixed_precision.experimental.policy import set_policy

tensorflow/python/keras/optimizers.py

@@ -794,6 +794,8 @@ def deserialize(config, custom_objects=None):
   Returns:
       A Keras Optimizer instance.
   """
+  # loss_scale_optimizer has a direct dependency of optimizer, import here
+  # rather than top to avoid the cyclic dependency.
   from tensorflow.python.keras.mixed_precision.experimental import loss_scale_optimizer  # pylint: disable=g-import-not-at-top
   all_classes = {
       'adadelta': adadelta_v2.Adadelta,

tensorflow/python/keras/utils/multi_gpu_utils.py

@@ -20,6 +20,8 @@ from __future__ import print_function
 from tensorflow.python.framework import ops
 from tensorflow.python.keras import backend as K
 from tensorflow.python.keras.engine.training import Model
+from tensorflow.python.keras.layers.core import Lambda
+from tensorflow.python.keras.layers.merge import concatenate
 from tensorflow.python.ops import array_ops
 from tensorflow.python.util import deprecation
 from tensorflow.python.util.tf_export import keras_export
@@ -152,10 +154,6 @@ def multi_gpu_model(model, gpus, cpu_merge=True, cpu_relocation=False):
   Raises:
     ValueError: if the `gpus` argument does not match available devices.
   """
-  # pylint: disable=g-import-not-at-top
-  from tensorflow.python.keras.layers.core import Lambda
-  from tensorflow.python.keras.layers.merge import concatenate
-
   if isinstance(gpus, (list, tuple)):
     if len(gpus) <= 1:
       raise ValueError('For multi-gpu usage to be effective, '