# ==============================================================================
"""Contains the spatial dropout layers."""

# pylint: disable=g-classes-have-attributes,g-direct-tensorflow-import,g-bad-import-order
import tensorflow.compat.v2 as tf
# pylint: enable=g-bad-import-order

import keras.backend as K
from keras.engine.input_spec import InputSpec

            

Reported by Pylint.

              # ==============================================================================
"""Contains the spatial dropout layers."""

# pylint: disable=g-classes-have-attributes,g-direct-tensorflow-import,g-bad-import-order
import tensorflow.compat.v2 as tf
# pylint: enable=g-bad-import-order

import keras.backend as K
from keras.engine.input_spec import InputSpec

            

Reported by Pylint.

              # ==============================================================================
"""Contains the spatial dropout layers."""

# pylint: disable=g-classes-have-attributes,g-direct-tensorflow-import,g-bad-import-order
import tensorflow.compat.v2 as tf
# pylint: enable=g-bad-import-order

import keras.backend as K
from keras.engine.input_spec import InputSpec

            

Reported by Pylint.

              """Contains the spatial dropout layers."""

# pylint: disable=g-classes-have-attributes,g-direct-tensorflow-import,g-bad-import-order
import tensorflow.compat.v2 as tf
# pylint: enable=g-bad-import-order

import keras.backend as K
from keras.engine.input_spec import InputSpec
from keras.layers.core.dropout import Dropout

            

Reported by Pylint.

              
# pylint: disable=g-classes-have-attributes,g-direct-tensorflow-import,g-bad-import-order
import tensorflow.compat.v2 as tf
# pylint: enable=g-bad-import-order

import keras.backend as K
from keras.engine.input_spec import InputSpec
from keras.layers.core.dropout import Dropout


            

Reported by Pylint.

              from keras.engine.input_spec import InputSpec
from keras.layers.core.dropout import Dropout

from tensorflow.python.util.tf_export import keras_export


@keras_export('keras.layers.SpatialDropout1D')
class SpatialDropout1D(Dropout):
  """Spatial 1D version of Dropout.

            

Reported by Pylint.

              
@keras_export('keras.layers.SpatialDropout1D')
class SpatialDropout1D(Dropout):
  """Spatial 1D version of Dropout.

  This version performs the same function as Dropout, however, it drops
  entire 1D feature maps instead of individual elements. If adjacent frames
  within feature maps are strongly correlated (as is normally the case in
  early convolution layers) then regular dropout will not regularize the

            

Reported by Pylint.

                    Networks](https://arxiv.org/abs/1411.4280)
  """

  def __init__(self, rate, **kwargs):
    super(SpatialDropout1D, self).__init__(rate, **kwargs)
    self.input_spec = InputSpec(ndim=3)

  def _get_noise_shape(self, inputs):
    input_shape = tf.shape(inputs)

            

Reported by Pylint.

                """

  def __init__(self, rate, **kwargs):
    super(SpatialDropout1D, self).__init__(rate, **kwargs)
    self.input_spec = InputSpec(ndim=3)

  def _get_noise_shape(self, inputs):
    input_shape = tf.shape(inputs)
    noise_shape = (input_shape[0], 1, input_shape[2])

            

Reported by Pylint.

                """

  def __init__(self, rate, **kwargs):
    super(SpatialDropout1D, self).__init__(rate, **kwargs)
    self.input_spec = InputSpec(ndim=3)

  def _get_noise_shape(self, inputs):
    input_shape = tf.shape(inputs)
    noise_shape = (input_shape[0], 1, input_shape[2])

            

Reported by Pylint.

              from __future__ import division
from __future__ import print_function

import tensorflow as tf

from keras.benchmarks import benchmark_util


class BidirectionalLSTMBenchmark(tf.test.Benchmark):

            

Reported by Pylint.

              
import tensorflow as tf

from keras.benchmarks import benchmark_util


class BidirectionalLSTMBenchmark(tf.test.Benchmark):
  """Benchmarks for Bidirectional LSTM using `tf.test.Benchmark`."""


            

Reported by Pylint.

              

class BidirectionalLSTMBenchmark(tf.test.Benchmark):
  """Benchmarks for Bidirectional LSTM using `tf.test.Benchmark`."""

  def __init__(self):
    super(BidirectionalLSTMBenchmark, self).__init__()
    self.max_feature = 20000
    self.max_len = 200

            

Reported by Pylint.

              class BidirectionalLSTMBenchmark(tf.test.Benchmark):
  """Benchmarks for Bidirectional LSTM using `tf.test.Benchmark`."""

  def __init__(self):
    super(BidirectionalLSTMBenchmark, self).__init__()
    self.max_feature = 20000
    self.max_len = 200
    (self.imdb_x, self.imdb_y), _ = tf.keras.datasets.imdb.load_data(
        num_words=self.max_feature)

            

Reported by Pylint.

                """Benchmarks for Bidirectional LSTM using `tf.test.Benchmark`."""

  def __init__(self):
    super(BidirectionalLSTMBenchmark, self).__init__()
    self.max_feature = 20000
    self.max_len = 200
    (self.imdb_x, self.imdb_y), _ = tf.keras.datasets.imdb.load_data(
        num_words=self.max_feature)
    self.imdb_x = tf.keras.preprocessing.sequence.pad_sequences(

            

Reported by Pylint.

                """Benchmarks for Bidirectional LSTM using `tf.test.Benchmark`."""

  def __init__(self):
    super(BidirectionalLSTMBenchmark, self).__init__()
    self.max_feature = 20000
    self.max_len = 200
    (self.imdb_x, self.imdb_y), _ = tf.keras.datasets.imdb.load_data(
        num_words=self.max_feature)
    self.imdb_x = tf.keras.preprocessing.sequence.pad_sequences(

            

Reported by Pylint.

              
  def __init__(self):
    super(BidirectionalLSTMBenchmark, self).__init__()
    self.max_feature = 20000
    self.max_len = 200
    (self.imdb_x, self.imdb_y), _ = tf.keras.datasets.imdb.load_data(
        num_words=self.max_feature)
    self.imdb_x = tf.keras.preprocessing.sequence.pad_sequences(
        self.imdb_x, maxlen=self.max_len)

            

Reported by Pylint.

                def __init__(self):
    super(BidirectionalLSTMBenchmark, self).__init__()
    self.max_feature = 20000
    self.max_len = 200
    (self.imdb_x, self.imdb_y), _ = tf.keras.datasets.imdb.load_data(
        num_words=self.max_feature)
    self.imdb_x = tf.keras.preprocessing.sequence.pad_sequences(
        self.imdb_x, maxlen=self.max_len)


            

Reported by Pylint.

                  super(BidirectionalLSTMBenchmark, self).__init__()
    self.max_feature = 20000
    self.max_len = 200
    (self.imdb_x, self.imdb_y), _ = tf.keras.datasets.imdb.load_data(
        num_words=self.max_feature)
    self.imdb_x = tf.keras.preprocessing.sequence.pad_sequences(
        self.imdb_x, maxlen=self.max_len)

  def _build_model(self):

            

Reported by Pylint.

                  self.max_len = 200
    (self.imdb_x, self.imdb_y), _ = tf.keras.datasets.imdb.load_data(
        num_words=self.max_feature)
    self.imdb_x = tf.keras.preprocessing.sequence.pad_sequences(
        self.imdb_x, maxlen=self.max_len)

  def _build_model(self):
    """Model from https://keras.io/examples/nlp/bidirectional_lstm_imdb/."""
    inputs = tf.keras.Input(shape=(None,), dtype='int32')

            

Reported by Pylint.

              from __future__ import division
from __future__ import print_function

import tensorflow as tf

import numpy as np

from keras.benchmarks import benchmark_util


            

Reported by Pylint.

              
import numpy as np

from keras.benchmarks import benchmark_util


class MLPReutersBenchmark(tf.test.Benchmark):
  """Benchmarks for MLP using `tf.test.Benchmark`."""


            

Reported by Pylint.

              

class MLPReutersBenchmark(tf.test.Benchmark):
  """Benchmarks for MLP using `tf.test.Benchmark`."""

  def __init__(self):
    super(MLPReutersBenchmark, self).__init__()
    self.max_words = 1000
    (self.x_train, self.y_train), _ = tf.keras.datasets.reuters.load_data(

            

Reported by Pylint.

              class MLPReutersBenchmark(tf.test.Benchmark):
  """Benchmarks for MLP using `tf.test.Benchmark`."""

  def __init__(self):
    super(MLPReutersBenchmark, self).__init__()
    self.max_words = 1000
    (self.x_train, self.y_train), _ = tf.keras.datasets.reuters.load_data(
        num_words=self.max_words)
    self.num_classes = np.max(self.y_train) + 1

            

Reported by Pylint.

                """Benchmarks for MLP using `tf.test.Benchmark`."""

  def __init__(self):
    super(MLPReutersBenchmark, self).__init__()
    self.max_words = 1000
    (self.x_train, self.y_train), _ = tf.keras.datasets.reuters.load_data(
        num_words=self.max_words)
    self.num_classes = np.max(self.y_train) + 1
    tokenizer = tf.keras.preprocessing.text.Tokenizer(num_words=self.max_words)

            

Reported by Pylint.

                """Benchmarks for MLP using `tf.test.Benchmark`."""

  def __init__(self):
    super(MLPReutersBenchmark, self).__init__()
    self.max_words = 1000
    (self.x_train, self.y_train), _ = tf.keras.datasets.reuters.load_data(
        num_words=self.max_words)
    self.num_classes = np.max(self.y_train) + 1
    tokenizer = tf.keras.preprocessing.text.Tokenizer(num_words=self.max_words)

            

Reported by Pylint.

              
  def __init__(self):
    super(MLPReutersBenchmark, self).__init__()
    self.max_words = 1000
    (self.x_train, self.y_train), _ = tf.keras.datasets.reuters.load_data(
        num_words=self.max_words)
    self.num_classes = np.max(self.y_train) + 1
    tokenizer = tf.keras.preprocessing.text.Tokenizer(num_words=self.max_words)
    self.x_train = tokenizer.sequences_to_matrix(self.x_train, mode='binary')

            

Reported by Pylint.

                def __init__(self):
    super(MLPReutersBenchmark, self).__init__()
    self.max_words = 1000
    (self.x_train, self.y_train), _ = tf.keras.datasets.reuters.load_data(
        num_words=self.max_words)
    self.num_classes = np.max(self.y_train) + 1
    tokenizer = tf.keras.preprocessing.text.Tokenizer(num_words=self.max_words)
    self.x_train = tokenizer.sequences_to_matrix(self.x_train, mode='binary')
    self.y_train = tf.keras.utils.to_categorical(self.y_train, self.num_classes)

            

Reported by Pylint.

                  self.max_words = 1000
    (self.x_train, self.y_train), _ = tf.keras.datasets.reuters.load_data(
        num_words=self.max_words)
    self.num_classes = np.max(self.y_train) + 1
    tokenizer = tf.keras.preprocessing.text.Tokenizer(num_words=self.max_words)
    self.x_train = tokenizer.sequences_to_matrix(self.x_train, mode='binary')
    self.y_train = tf.keras.utils.to_categorical(self.y_train, self.num_classes)
    self.epochs = 5


            

Reported by Pylint.

                  (self.x_train, self.y_train), _ = tf.keras.datasets.reuters.load_data(
        num_words=self.max_words)
    self.num_classes = np.max(self.y_train) + 1
    tokenizer = tf.keras.preprocessing.text.Tokenizer(num_words=self.max_words)
    self.x_train = tokenizer.sequences_to_matrix(self.x_train, mode='binary')
    self.y_train = tf.keras.utils.to_categorical(self.y_train, self.num_classes)
    self.epochs = 5

  def _build_model(self):

            

Reported by Pylint.

              # limitations under the License.
# ==============================================================================

from tensorflow.compiler.tests import xla_test

import tensorflow.compat.v2 as tf
from keras.engine import training
from keras.layers import core
from keras.optimizer_v2 import adam

            

Reported by Pylint.

              
from tensorflow.compiler.tests import xla_test

import tensorflow.compat.v2 as tf
from keras.engine import training
from keras.layers import core
from keras.optimizer_v2 import adam
from tensorflow.python.training.tracking import util as trackable_utils


            

Reported by Pylint.

              from tensorflow.compiler.tests import xla_test

import tensorflow.compat.v2 as tf
from keras.engine import training
from keras.layers import core
from keras.optimizer_v2 import adam
from tensorflow.python.training.tracking import util as trackable_utils



            

Reported by Pylint.

              
import tensorflow.compat.v2 as tf
from keras.engine import training
from keras.layers import core
from keras.optimizer_v2 import adam
from tensorflow.python.training.tracking import util as trackable_utils


class NonLayerTrackable(tf.Module):

            

Reported by Pylint.

              import tensorflow.compat.v2 as tf
from keras.engine import training
from keras.layers import core
from keras.optimizer_v2 import adam
from tensorflow.python.training.tracking import util as trackable_utils


class NonLayerTrackable(tf.Module):


            

Reported by Pylint.

              from keras.engine import training
from keras.layers import core
from keras.optimizer_v2 import adam
from tensorflow.python.training.tracking import util as trackable_utils


class NonLayerTrackable(tf.Module):

  def __init__(self):

            

Reported by Pylint.

              # Copyright 2017 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

            

Reported by Pylint.

              from keras.engine import training
from keras.layers import core
from keras.optimizer_v2 import adam
from tensorflow.python.training.tracking import util as trackable_utils


class NonLayerTrackable(tf.Module):

  def __init__(self):

            

Reported by Pylint.

              from tensorflow.python.training.tracking import util as trackable_utils


class NonLayerTrackable(tf.Module):

  def __init__(self):
    super(NonLayerTrackable, self).__init__()
    self.a_variable = trackable_utils.add_variable(
        self, name="a_variable", shape=[])

            

Reported by Pylint.

              from tensorflow.python.training.tracking import util as trackable_utils


class NonLayerTrackable(tf.Module):

  def __init__(self):
    super(NonLayerTrackable, self).__init__()
    self.a_variable = trackable_utils.add_variable(
        self, name="a_variable", shape=[])

            

Reported by Pylint.

              from __future__ import division
from __future__ import print_function

import tensorflow as tf

from keras.benchmarks import benchmark_util


class HierarchicalRNNBenchmark(tf.test.Benchmark):

            

Reported by Pylint.

              
import tensorflow as tf

from keras.benchmarks import benchmark_util


class HierarchicalRNNBenchmark(tf.test.Benchmark):
  """Benchmarks for Hierarchical RNN using `tf.test.Benchmark`."""


            

Reported by Pylint.

              

class HierarchicalRNNBenchmark(tf.test.Benchmark):
  """Benchmarks for Hierarchical RNN using `tf.test.Benchmark`."""

  def __init__(self):
    super(HierarchicalRNNBenchmark, self).__init__()
    self.num_classes = 10
    self.row_hidden, self.col_hidden = 128, 128

            

Reported by Pylint.

              class HierarchicalRNNBenchmark(tf.test.Benchmark):
  """Benchmarks for Hierarchical RNN using `tf.test.Benchmark`."""

  def __init__(self):
    super(HierarchicalRNNBenchmark, self).__init__()
    self.num_classes = 10
    self.row_hidden, self.col_hidden = 128, 128
    (self.x_train, self.y_train), _ = tf.keras.datasets.mnist.load_data()
    self.x_train = self.x_train.reshape(self.x_train.shape[0], 28, 28, 1)

            

Reported by Pylint.

                """Benchmarks for Hierarchical RNN using `tf.test.Benchmark`."""

  def __init__(self):
    super(HierarchicalRNNBenchmark, self).__init__()
    self.num_classes = 10
    self.row_hidden, self.col_hidden = 128, 128
    (self.x_train, self.y_train), _ = tf.keras.datasets.mnist.load_data()
    self.x_train = self.x_train.reshape(self.x_train.shape[0], 28, 28, 1)
    self.x_train = self.x_train.astype('float32') / 255

            

Reported by Pylint.

                """Benchmarks for Hierarchical RNN using `tf.test.Benchmark`."""

  def __init__(self):
    super(HierarchicalRNNBenchmark, self).__init__()
    self.num_classes = 10
    self.row_hidden, self.col_hidden = 128, 128
    (self.x_train, self.y_train), _ = tf.keras.datasets.mnist.load_data()
    self.x_train = self.x_train.reshape(self.x_train.shape[0], 28, 28, 1)
    self.x_train = self.x_train.astype('float32') / 255

            

Reported by Pylint.

              
  def __init__(self):
    super(HierarchicalRNNBenchmark, self).__init__()
    self.num_classes = 10
    self.row_hidden, self.col_hidden = 128, 128
    (self.x_train, self.y_train), _ = tf.keras.datasets.mnist.load_data()
    self.x_train = self.x_train.reshape(self.x_train.shape[0], 28, 28, 1)
    self.x_train = self.x_train.astype('float32') / 255
    self.y_train = tf.keras.utils.to_categorical(self.y_train, self.num_classes)

            

Reported by Pylint.

                def __init__(self):
    super(HierarchicalRNNBenchmark, self).__init__()
    self.num_classes = 10
    self.row_hidden, self.col_hidden = 128, 128
    (self.x_train, self.y_train), _ = tf.keras.datasets.mnist.load_data()
    self.x_train = self.x_train.reshape(self.x_train.shape[0], 28, 28, 1)
    self.x_train = self.x_train.astype('float32') / 255
    self.y_train = tf.keras.utils.to_categorical(self.y_train, self.num_classes)


            

Reported by Pylint.

                  super(HierarchicalRNNBenchmark, self).__init__()
    self.num_classes = 10
    self.row_hidden, self.col_hidden = 128, 128
    (self.x_train, self.y_train), _ = tf.keras.datasets.mnist.load_data()
    self.x_train = self.x_train.reshape(self.x_train.shape[0], 28, 28, 1)
    self.x_train = self.x_train.astype('float32') / 255
    self.y_train = tf.keras.utils.to_categorical(self.y_train, self.num_classes)

  def _build_model(self):

            

Reported by Pylint.

                  self.num_classes = 10
    self.row_hidden, self.col_hidden = 128, 128
    (self.x_train, self.y_train), _ = tf.keras.datasets.mnist.load_data()
    self.x_train = self.x_train.reshape(self.x_train.shape[0], 28, 28, 1)
    self.x_train = self.x_train.astype('float32') / 255
    self.y_train = tf.keras.utils.to_categorical(self.y_train, self.num_classes)

  def _build_model(self):
    """Model from https://github.com/keras-team/keras/blob/master/examples

            

Reported by Pylint.

              
from keras.preprocessing.sequence import _remove_long_seq
from keras.utils.data_utils import get_file
from tensorflow.python.platform import tf_logging as logging
from tensorflow.python.util.tf_export import keras_export


@keras_export('keras.datasets.reuters.load_data')
def load_data(path='reuters.npz',

            

Reported by Pylint.

              from keras.preprocessing.sequence import _remove_long_seq
from keras.utils.data_utils import get_file
from tensorflow.python.platform import tf_logging as logging
from tensorflow.python.util.tf_export import keras_export


@keras_export('keras.datasets.reuters.load_data')
def load_data(path='reuters.npz',
              num_words=None,

            

Reported by Pylint.

                with np.load(path, allow_pickle=True) as f:  # pylint: disable=unexpected-keyword-arg
    xs, labels = f['x'], f['y']

  rng = np.random.RandomState(seed)
  indices = np.arange(len(xs))
  rng.shuffle(indices)
  xs = xs[indices]
  labels = labels[indices]


            

Reported by Pylint.

              

@keras_export('keras.datasets.reuters.load_data')
def load_data(path='reuters.npz',
              num_words=None,
              skip_top=0,
              maxlen=None,
              test_split=0.2,
              seed=113,

            

Reported by Pylint.

              

@keras_export('keras.datasets.reuters.load_data')
def load_data(path='reuters.npz',
              num_words=None,
              skip_top=0,
              maxlen=None,
              test_split=0.2,
              seed=113,

            

Reported by Pylint.

                            oov_char=2,
              index_from=3,
              **kwargs):
  """Loads the Reuters newswire classification dataset.

  This is a dataset of 11,228 newswires from Reuters, labeled over 46 topics.

  This was originally generated by parsing and preprocessing the classic
  Reuters-21578 dataset, but the preprocessing code is no longer packaged

            

Reported by Pylint.

                have simply been skipped.
  """
  # Legacy support
  if 'nb_words' in kwargs:
    logging.warning('The `nb_words` argument in `load_data` '
                    'has been renamed `num_words`.')
    num_words = kwargs.pop('nb_words')
  if kwargs:
    raise TypeError(f'Unrecognized keyword arguments: {str(kwargs)}')

            

Reported by Pylint.

                """
  # Legacy support
  if 'nb_words' in kwargs:
    logging.warning('The `nb_words` argument in `load_data` '
                    'has been renamed `num_words`.')
    num_words = kwargs.pop('nb_words')
  if kwargs:
    raise TypeError(f'Unrecognized keyword arguments: {str(kwargs)}')


            

Reported by Pylint.

                if 'nb_words' in kwargs:
    logging.warning('The `nb_words` argument in `load_data` '
                    'has been renamed `num_words`.')
    num_words = kwargs.pop('nb_words')
  if kwargs:
    raise TypeError(f'Unrecognized keyword arguments: {str(kwargs)}')

  origin_folder = 'https://storage.googleapis.com/tensorflow/tf-keras-datasets/'
  path = get_file(

            

Reported by Pylint.

                  logging.warning('The `nb_words` argument in `load_data` '
                    'has been renamed `num_words`.')
    num_words = kwargs.pop('nb_words')
  if kwargs:
    raise TypeError(f'Unrecognized keyword arguments: {str(kwargs)}')

  origin_folder = 'https://storage.googleapis.com/tensorflow/tf-keras-datasets/'
  path = get_file(
      path,

            

Reported by Pylint.

              # ==============================================================================
"""Adadelta optimizer implementation."""

import tensorflow.compat.v2 as tf
# pylint: disable=g-classes-have-attributes

import numpy as np
from keras import backend_config
from keras.optimizer_v2 import optimizer_v2

            

Reported by Pylint.

              """Adadelta optimizer implementation."""

import tensorflow.compat.v2 as tf
# pylint: disable=g-classes-have-attributes

import numpy as np
from keras import backend_config
from keras.optimizer_v2 import optimizer_v2
from tensorflow.python.util.tf_export import keras_export

            

Reported by Pylint.

              # pylint: disable=g-classes-have-attributes

import numpy as np
from keras import backend_config
from keras.optimizer_v2 import optimizer_v2
from tensorflow.python.util.tf_export import keras_export


@keras_export('keras.optimizers.Adadelta')

            

Reported by Pylint.

              
import numpy as np
from keras import backend_config
from keras.optimizer_v2 import optimizer_v2
from tensorflow.python.util.tf_export import keras_export


@keras_export('keras.optimizers.Adadelta')
class Adadelta(optimizer_v2.OptimizerV2):

            

Reported by Pylint.

              import numpy as np
from keras import backend_config
from keras.optimizer_v2 import optimizer_v2
from tensorflow.python.util.tf_export import keras_export


@keras_export('keras.optimizers.Adadelta')
class Adadelta(optimizer_v2.OptimizerV2):
  r"""Optimizer that implements the Adadelta algorithm.

            

Reported by Pylint.

              
@keras_export('keras.optimizers.Adadelta')
class Adadelta(optimizer_v2.OptimizerV2):
  r"""Optimizer that implements the Adadelta algorithm.

  Adadelta optimization is a stochastic gradient descent method that is based on
  adaptive learning rate per dimension to address two drawbacks:

  - The continual decay of learning rates throughout training.

            

Reported by Pylint.

                  - [Zeiler, 2012](http://arxiv.org/abs/1212.5701)
  """

  _HAS_AGGREGATE_GRAD = True

  def __init__(self,
               learning_rate=0.001,
               rho=0.95,
               epsilon=1e-7,

            

Reported by Pylint.

              
  _HAS_AGGREGATE_GRAD = True

  def __init__(self,
               learning_rate=0.001,
               rho=0.95,
               epsilon=1e-7,
               name='Adadelta',
               **kwargs):

            

Reported by Pylint.

                             epsilon=1e-7,
               name='Adadelta',
               **kwargs):
    super(Adadelta, self).__init__(name, **kwargs)
    self._set_hyper('learning_rate', kwargs.get('lr', learning_rate))
    self._set_hyper('decay', self._initial_decay)
    self._set_hyper('rho', rho)
    self.epsilon = epsilon or backend_config.epsilon()


            

Reported by Pylint.

                             epsilon=1e-7,
               name='Adadelta',
               **kwargs):
    super(Adadelta, self).__init__(name, **kwargs)
    self._set_hyper('learning_rate', kwargs.get('lr', learning_rate))
    self._set_hyper('decay', self._initial_decay)
    self._set_hyper('rho', rho)
    self.epsilon = epsilon or backend_config.epsilon()


            

Reported by Pylint.

              from keras.layers import recurrent
from keras.layers.legacy_rnn import rnn_cell_wrapper_impl
from keras.utils import tf_inspect
from tensorflow.python.util.tf_export import tf_export


class _RNNCellWrapperV2(recurrent.AbstractRNNCell):
  """Base class for cells wrappers V2 compatibility.


            

Reported by Pylint.

                @classmethod
  def from_config(cls, config, custom_objects=None):
    config = config.copy()
    from keras.layers.serialization import deserialize as deserialize_layer  # pylint: disable=g-import-not-at-top
    cell = deserialize_layer(config.pop("cell"), custom_objects=custom_objects)
    return cls(cell, **config)


@tf_export("nn.RNNCellDropoutWrapper", v1=[])

            

Reported by Pylint.

              from tensorflow.python.util.tf_export import tf_export


class _RNNCellWrapperV2(recurrent.AbstractRNNCell):
  """Base class for cells wrappers V2 compatibility.

  This class along with `rnn_cell_impl._RNNCellWrapperV1` allows to define
  wrappers that are compatible with V1 and V2, and defines helper methods for
  this purpose.

            

Reported by Pylint.

              from tensorflow.python.util.tf_export import tf_export


class _RNNCellWrapperV2(recurrent.AbstractRNNCell):
  """Base class for cells wrappers V2 compatibility.

  This class along with `rnn_cell_impl._RNNCellWrapperV1` allows to define
  wrappers that are compatible with V1 and V2, and defines helper methods for
  this purpose.

            

Reported by Pylint.

                      cell_call_spec.varkw is not None
    )

  def call(self, inputs, state, **kwargs):
    """Runs the RNN cell step computation.

    When `call` is being used, we assume that the wrapper object has been built,
    and therefore the wrapped cells has been built via its `build` method and
    its `call` method can be used directly.

            

Reported by Pylint.

                  return self._call_wrapped_cell(
        inputs, state, cell_call_fn=self.cell.call, **kwargs)

  def build(self, inputs_shape):
    """Builds the wrapped cell."""
    self.cell.build(inputs_shape)
    self.built = True

  def get_config(self):

            

Reported by Pylint.

                  return dict(list(base_config.items()) + list(config.items()))

  @classmethod
  def from_config(cls, config, custom_objects=None):
    config = config.copy()
    from keras.layers.serialization import deserialize as deserialize_layer  # pylint: disable=g-import-not-at-top
    cell = deserialize_layer(config.pop("cell"), custom_objects=custom_objects)
    return cls(cell, **config)


            

Reported by Pylint.

              

class _RNNCellWrapperV2(recurrent.AbstractRNNCell):
  """Base class for cells wrappers V2 compatibility.

  This class along with `rnn_cell_impl._RNNCellWrapperV1` allows to define
  wrappers that are compatible with V1 and V2, and defines helper methods for
  this purpose.
  """

            

Reported by Pylint.

                this purpose.
  """

  def __init__(self, cell, *args, **kwargs):
    super(_RNNCellWrapperV2, self).__init__(*args, **kwargs)
    self.cell = cell
    cell_call_spec = tf_inspect.getfullargspec(cell.call)
    self._expects_training_arg = ("training" in cell_call_spec.args) or (
        cell_call_spec.varkw is not None

            

Reported by Pylint.

                """

  def __init__(self, cell, *args, **kwargs):
    super(_RNNCellWrapperV2, self).__init__(*args, **kwargs)
    self.cell = cell
    cell_call_spec = tf_inspect.getfullargspec(cell.call)
    self._expects_training_arg = ("training" in cell_call_spec.args) or (
        cell_call_spec.varkw is not None
    )

            

Reported by Pylint.

              # ==============================================================================
"""Tests for KPL + CentralStorageStrategy."""

from absl.testing import parameterized
import tensorflow as tf

from tensorflow.python.distribute import combinations as ds_combinations
from tensorflow.python.distribute import strategy_combinations
from tensorflow.python.framework import test_combinations as combinations

            

Reported by Pylint.

              """Tests for KPL + CentralStorageStrategy."""

from absl.testing import parameterized
import tensorflow as tf

from tensorflow.python.distribute import combinations as ds_combinations
from tensorflow.python.distribute import strategy_combinations
from tensorflow.python.framework import test_combinations as combinations
from tensorflow.python.keras.utils import kpl_test_utils

            

Reported by Pylint.

              from absl.testing import parameterized
import tensorflow as tf

from tensorflow.python.distribute import combinations as ds_combinations
from tensorflow.python.distribute import strategy_combinations
from tensorflow.python.framework import test_combinations as combinations
from tensorflow.python.keras.utils import kpl_test_utils



            

Reported by Pylint.

              import tensorflow as tf

from tensorflow.python.distribute import combinations as ds_combinations
from tensorflow.python.distribute import strategy_combinations
from tensorflow.python.framework import test_combinations as combinations
from tensorflow.python.keras.utils import kpl_test_utils


# TODO(b/182278926): Combine this test with other strategies.

            

Reported by Pylint.

              
from tensorflow.python.distribute import combinations as ds_combinations
from tensorflow.python.distribute import strategy_combinations
from tensorflow.python.framework import test_combinations as combinations
from tensorflow.python.keras.utils import kpl_test_utils


# TODO(b/182278926): Combine this test with other strategies.
@ds_combinations.generate(

            

Reported by Pylint.

              from tensorflow.python.distribute import combinations as ds_combinations
from tensorflow.python.distribute import strategy_combinations
from tensorflow.python.framework import test_combinations as combinations
from tensorflow.python.keras.utils import kpl_test_utils


# TODO(b/182278926): Combine this test with other strategies.
@ds_combinations.generate(
    combinations.combine(

            

Reported by Pylint.

              from tensorflow.python.keras.utils import kpl_test_utils


# TODO(b/182278926): Combine this test with other strategies.
@ds_combinations.generate(
    combinations.combine(
        distribution=[
            strategy_combinations.central_storage_strategy_with_gpu_and_cpu,
        ],

            

Reported by Pylint.

                          strategy_combinations.central_storage_strategy_with_gpu_and_cpu,
        ],
        mode=["eager"]))
class CentralStorageStrategyTest(tf.test.TestCase, parameterized.TestCase):

  def testTrainAndServeWithKPL(self, distribution):
    use_adapt = False
    test_utils_obj = kpl_test_utils.DistributeKplTestUtils()
    with distribution.scope():

            

Reported by Pylint.

                          strategy_combinations.central_storage_strategy_with_gpu_and_cpu,
        ],
        mode=["eager"]))
class CentralStorageStrategyTest(tf.test.TestCase, parameterized.TestCase):

  def testTrainAndServeWithKPL(self, distribution):
    use_adapt = False
    test_utils_obj = kpl_test_utils.DistributeKplTestUtils()
    with distribution.scope():

            

Reported by Pylint.

                      mode=["eager"]))
class CentralStorageStrategyTest(tf.test.TestCase, parameterized.TestCase):

  def testTrainAndServeWithKPL(self, distribution):
    use_adapt = False
    test_utils_obj = kpl_test_utils.DistributeKplTestUtils()
    with distribution.scope():
      feature_mapper, label_mapper = test_utils_obj.define_kpls_for_training(
          use_adapt)

            

Reported by Pylint.

              from __future__ import division
from __future__ import print_function

import tensorflow as tf

from keras.benchmarks import benchmark_util


class IRNNMnistBenchmark(tf.test.Benchmark):

            

Reported by Pylint.

              
import tensorflow as tf

from keras.benchmarks import benchmark_util


class IRNNMnistBenchmark(tf.test.Benchmark):
  """Benchmarks for IRNN using `tf.test.Benchmark`."""


            

Reported by Pylint.

              

class IRNNMnistBenchmark(tf.test.Benchmark):
  """Benchmarks for IRNN using `tf.test.Benchmark`."""

  def __init__(self):
    super(IRNNMnistBenchmark, self).__init__()
    self.num_classes = 10
    self.hidden_units = 100

            

Reported by Pylint.

              class IRNNMnistBenchmark(tf.test.Benchmark):
  """Benchmarks for IRNN using `tf.test.Benchmark`."""

  def __init__(self):
    super(IRNNMnistBenchmark, self).__init__()
    self.num_classes = 10
    self.hidden_units = 100
    self.learning_rate = 1e-6
    (self.x_train, self.y_train), _ = tf.keras.datasets.mnist.load_data()

            

Reported by Pylint.

                """Benchmarks for IRNN using `tf.test.Benchmark`."""

  def __init__(self):
    super(IRNNMnistBenchmark, self).__init__()
    self.num_classes = 10
    self.hidden_units = 100
    self.learning_rate = 1e-6
    (self.x_train, self.y_train), _ = tf.keras.datasets.mnist.load_data()
    self.x_train = self.x_train.reshape(self.x_train.shape[0], -1, 1)

            

Reported by Pylint.

                """Benchmarks for IRNN using `tf.test.Benchmark`."""

  def __init__(self):
    super(IRNNMnistBenchmark, self).__init__()
    self.num_classes = 10
    self.hidden_units = 100
    self.learning_rate = 1e-6
    (self.x_train, self.y_train), _ = tf.keras.datasets.mnist.load_data()
    self.x_train = self.x_train.reshape(self.x_train.shape[0], -1, 1)

            

Reported by Pylint.

              
  def __init__(self):
    super(IRNNMnistBenchmark, self).__init__()
    self.num_classes = 10
    self.hidden_units = 100
    self.learning_rate = 1e-6
    (self.x_train, self.y_train), _ = tf.keras.datasets.mnist.load_data()
    self.x_train = self.x_train.reshape(self.x_train.shape[0], -1, 1)
    self.x_train = self.x_train.astype('float32') / 255

            

Reported by Pylint.

                def __init__(self):
    super(IRNNMnistBenchmark, self).__init__()
    self.num_classes = 10
    self.hidden_units = 100
    self.learning_rate = 1e-6
    (self.x_train, self.y_train), _ = tf.keras.datasets.mnist.load_data()
    self.x_train = self.x_train.reshape(self.x_train.shape[0], -1, 1)
    self.x_train = self.x_train.astype('float32') / 255
    self.y_train = tf.keras.utils.to_categorical(self.y_train, self.num_classes)

            

Reported by Pylint.

                  super(IRNNMnistBenchmark, self).__init__()
    self.num_classes = 10
    self.hidden_units = 100
    self.learning_rate = 1e-6
    (self.x_train, self.y_train), _ = tf.keras.datasets.mnist.load_data()
    self.x_train = self.x_train.reshape(self.x_train.shape[0], -1, 1)
    self.x_train = self.x_train.astype('float32') / 255
    self.y_train = tf.keras.utils.to_categorical(self.y_train, self.num_classes)


            

Reported by Pylint.

                  self.num_classes = 10
    self.hidden_units = 100
    self.learning_rate = 1e-6
    (self.x_train, self.y_train), _ = tf.keras.datasets.mnist.load_data()
    self.x_train = self.x_train.reshape(self.x_train.shape[0], -1, 1)
    self.x_train = self.x_train.astype('float32') / 255
    self.y_train = tf.keras.utils.to_categorical(self.y_train, self.num_classes)

  def _build_model(self):

            

Reported by Pylint.