from __future__ import division
from __future__ import print_function

import tensorflow as tf

import tempfile
import time



            

Reported by Pylint.

              
import tensorflow as tf

import tempfile
import time


def save_and_load_benchmark(app):
  """Util for saved model benchmarks."""

            

Reported by Pylint.

              import tensorflow as tf

import tempfile
import time


def save_and_load_benchmark(app):
  """Util for saved model benchmarks."""
  trials = 3

            

Reported by Pylint.

              

def save_and_load_benchmark(app):
  """Util for saved model benchmarks."""
  trials = 3

  model = app(weights=None)
  model_name = app.__name__


            

Reported by Pylint.

              
def save_and_load_benchmark(app):
  """Util for saved model benchmarks."""
  trials = 3

  model = app(weights=None)
  model_name = app.__name__

  tmp_dir = tf.compat.v1.test.get_temp_dir()

            

Reported by Pylint.

                """Util for saved model benchmarks."""
  trials = 3

  model = app(weights=None)
  model_name = app.__name__

  tmp_dir = tf.compat.v1.test.get_temp_dir()
  tf.io.gfile.makedirs(tmp_dir)
  save_dir = tempfile.mkdtemp(dir=tmp_dir)

            

Reported by Pylint.

                trials = 3

  model = app(weights=None)
  model_name = app.__name__

  tmp_dir = tf.compat.v1.test.get_temp_dir()
  tf.io.gfile.makedirs(tmp_dir)
  save_dir = tempfile.mkdtemp(dir=tmp_dir)


            

Reported by Pylint.

                model = app(weights=None)
  model_name = app.__name__

  tmp_dir = tf.compat.v1.test.get_temp_dir()
  tf.io.gfile.makedirs(tmp_dir)
  save_dir = tempfile.mkdtemp(dir=tmp_dir)

  total_save_time = 0
  total_load_time = 0

            

Reported by Pylint.

                model_name = app.__name__

  tmp_dir = tf.compat.v1.test.get_temp_dir()
  tf.io.gfile.makedirs(tmp_dir)
  save_dir = tempfile.mkdtemp(dir=tmp_dir)

  total_save_time = 0
  total_load_time = 0


            

Reported by Pylint.

              
  tmp_dir = tf.compat.v1.test.get_temp_dir()
  tf.io.gfile.makedirs(tmp_dir)
  save_dir = tempfile.mkdtemp(dir=tmp_dir)

  total_save_time = 0
  total_load_time = 0

  # Run one untimed iteration of saving/loading.

            

Reported by Pylint.

              # ==============================================================================
"""Keras string lookup preprocessing layer."""

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

import numpy as np
from keras.engine import base_preprocessing_layer
from keras.layers.preprocessing import index_lookup

            

Reported by Pylint.

              """Keras string lookup preprocessing layer."""

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

import numpy as np
from keras.engine import base_preprocessing_layer
from keras.layers.preprocessing import index_lookup
from tensorflow.python.util.tf_export import keras_export

            

Reported by Pylint.

              import numpy as np
from keras.engine import base_preprocessing_layer
from keras.layers.preprocessing import index_lookup
from tensorflow.python.util.tf_export import keras_export


@keras_export(
    "keras.layers.StringLookup",
    "keras.layers.experimental.preprocessing.StringLookup",

            

Reported by Pylint.

                  "keras.layers.experimental.preprocessing.StringLookup",
    v1=[])
class StringLookup(index_lookup.IndexLookup):
  """Maps strings from a vocabulary to integer indices.

  This layer translates a set of arbitrary strings into an integer output via a
  table-based vocabulary lookup.

  The vocabulary for the layer must be either supplied on construction or

            

Reported by Pylint.

                vocabulary either directly or via `adapt()` before calling `get_vocabulary()`.
  """

  def __init__(self,
               max_tokens=None,
               num_oov_indices=1,
               mask_token=None,
               oov_token="[UNK]",
               vocabulary=None,

            

Reported by Pylint.

                vocabulary either directly or via `adapt()` before calling `get_vocabulary()`.
  """

  def __init__(self,
               max_tokens=None,
               num_oov_indices=1,
               mask_token=None,
               oov_token="[UNK]",
               vocabulary=None,

            

Reported by Bandit.

                vocabulary either directly or via `adapt()` before calling `get_vocabulary()`.
  """

  def __init__(self,
               max_tokens=None,
               num_oov_indices=1,
               mask_token=None,
               oov_token="[UNK]",
               vocabulary=None,

            

Reported by Pylint.

                             sparse=False,
               pad_to_max_tokens=False,
               **kwargs):
    allowed_dtypes = [tf.string]

    if "dtype" in kwargs and kwargs["dtype"] not in allowed_dtypes:
      raise ValueError(
          f"The value of the `dtype` for `StringLookup` may "
          f"only be one of {allowed_dtypes}, but received {kwargs['dtype']}.")

            

Reported by Pylint.

                             **kwargs):
    allowed_dtypes = [tf.string]

    if "dtype" in kwargs and kwargs["dtype"] not in allowed_dtypes:
      raise ValueError(
          f"The value of the `dtype` for `StringLookup` may "
          f"only be one of {allowed_dtypes}, but received {kwargs['dtype']}.")

    if "dtype" not in kwargs:

            

Reported by Pylint.

                  allowed_dtypes = [tf.string]

    if "dtype" in kwargs and kwargs["dtype"] not in allowed_dtypes:
      raise ValueError(
          f"The value of the `dtype` for `StringLookup` may "
          f"only be one of {allowed_dtypes}, but received {kwargs['dtype']}.")

    if "dtype" not in kwargs:
      kwargs["dtype"] = tf.string

            

Reported by Pylint.

              # ==============================================================================
"""Keras backend config API."""

import tensorflow.compat.v2 as tf
from tensorflow.python.util.tf_export import keras_export

# The type of float to use throughout a session.
_FLOATX = 'float32'


            

Reported by Pylint.

              """Keras backend config API."""

import tensorflow.compat.v2 as tf
from tensorflow.python.util.tf_export import keras_export

# The type of float to use throughout a session.
_FLOATX = 'float32'

# Epsilon fuzz factor used throughout the codebase.

            

Reported by Pylint.

                1e-05
   >>> tf.keras.backend.set_epsilon(1e-7)
  """
  global _EPSILON
  _EPSILON = value


@keras_export('keras.backend.floatx')
def floatx():

            

Reported by Pylint.

                Raises:
      ValueError: In case of invalid value.
  """
  global _FLOATX
  accepted_dtypes = {'float16', 'float32', 'float64'}
  if value not in accepted_dtypes:
    raise ValueError(
        f'Unknown `floatx` value: {value}. Expected one of {accepted_dtypes}')
  _FLOATX = str(value)

            

Reported by Pylint.

                Raises:
      ValueError: In case of invalid `data_format` value.
  """
  global _IMAGE_DATA_FORMAT
  accepted_formats = {'channels_last', 'channels_first'}
  if data_format not in accepted_formats:
    raise ValueError(
        f'Unknown `data_format`: {data_format}. '
        f'Expected one of {accepted_formats}')

            

Reported by Pylint.

              @keras_export('keras.backend.epsilon')
@tf.__internal__.dispatch.add_dispatch_support
def epsilon():
  """Returns the value of the fuzz factor used in numeric expressions.

  Returns:
      A float.

  Example:

            

Reported by Pylint.

                >>> tf.keras.backend.epsilon()
  1e-07
  """
  return _EPSILON


@keras_export('keras.backend.set_epsilon')
def set_epsilon(value):
  """Sets the value of the fuzz factor used in numeric expressions.

            

Reported by Pylint.

              
@keras_export('keras.backend.set_epsilon')
def set_epsilon(value):
  """Sets the value of the fuzz factor used in numeric expressions.

  Args:
      value: float. New value of epsilon.

  Example:

            

Reported by Pylint.

                1e-05
   >>> tf.keras.backend.set_epsilon(1e-7)
  """
  global _EPSILON
  _EPSILON = value


@keras_export('keras.backend.floatx')
def floatx():

            

Reported by Pylint.

                 >>> tf.keras.backend.set_epsilon(1e-7)
  """
  global _EPSILON
  _EPSILON = value


@keras_export('keras.backend.floatx')
def floatx():
  """Returns the default float type, as a string.

            

Reported by Pylint.

              import numpy as np

from keras.utils.data_utils import get_file
from tensorflow.python.util.tf_export import keras_export


@keras_export('keras.datasets.boston_housing.load_data')
def load_data(path='boston_housing.npz', test_split=0.2, seed=113):
  """Loads the Boston Housing dataset.

            

Reported by Pylint.

                  x = f['x']
    y = f['y']

  rng = np.random.RandomState(seed)
  indices = np.arange(len(x))
  rng.shuffle(indices)
  x = x[indices]
  y = y[indices]


            

Reported by Pylint.

              
@keras_export('keras.datasets.boston_housing.load_data')
def load_data(path='boston_housing.npz', test_split=0.2, seed=113):
  """Loads the Boston Housing dataset.

  This is a dataset taken from the StatLib library which is maintained at
  Carnegie Mellon University.

  Samples contain 13 attributes of houses at different locations around the

            

Reported by Pylint.

                  target scalars. The targets are float scalars typically between 10 and
    50 that represent the home prices in k$.
  """
  assert 0 <= test_split < 1
  origin_folder = 'https://storage.googleapis.com/tensorflow/tf-keras-datasets/'
  path = get_file(
      path,
      origin=origin_folder + 'boston_housing.npz',
      file_hash=

            

Reported by Bandit.

                  target scalars. The targets are float scalars typically between 10 and
    50 that represent the home prices in k$.
  """
  assert 0 <= test_split < 1
  origin_folder = 'https://storage.googleapis.com/tensorflow/tf-keras-datasets/'
  path = get_file(
      path,
      origin=origin_folder + 'boston_housing.npz',
      file_hash=

            

Reported by Pylint.

                  50 that represent the home prices in k$.
  """
  assert 0 <= test_split < 1
  origin_folder = 'https://storage.googleapis.com/tensorflow/tf-keras-datasets/'
  path = get_file(
      path,
      origin=origin_folder + 'boston_housing.npz',
      file_hash=
      'f553886a1f8d56431e820c5b82552d9d95cfcb96d1e678153f8839538947dff5')

            

Reported by Pylint.

                """
  assert 0 <= test_split < 1
  origin_folder = 'https://storage.googleapis.com/tensorflow/tf-keras-datasets/'
  path = get_file(
      path,
      origin=origin_folder + 'boston_housing.npz',
      file_hash=
      'f553886a1f8d56431e820c5b82552d9d95cfcb96d1e678153f8839538947dff5')
  with np.load(path, allow_pickle=True) as f:  # pylint: disable=unexpected-keyword-arg

            

Reported by Pylint.

                    origin=origin_folder + 'boston_housing.npz',
      file_hash=
      'f553886a1f8d56431e820c5b82552d9d95cfcb96d1e678153f8839538947dff5')
  with np.load(path, allow_pickle=True) as f:  # pylint: disable=unexpected-keyword-arg
    x = f['x']
    y = f['y']

  rng = np.random.RandomState(seed)
  indices = np.arange(len(x))

            

Reported by Pylint.

                    origin=origin_folder + 'boston_housing.npz',
      file_hash=
      'f553886a1f8d56431e820c5b82552d9d95cfcb96d1e678153f8839538947dff5')
  with np.load(path, allow_pickle=True) as f:  # pylint: disable=unexpected-keyword-arg
    x = f['x']
    y = f['y']

  rng = np.random.RandomState(seed)
  indices = np.arange(len(x))

            

Reported by Pylint.

                    file_hash=
      'f553886a1f8d56431e820c5b82552d9d95cfcb96d1e678153f8839538947dff5')
  with np.load(path, allow_pickle=True) as f:  # pylint: disable=unexpected-keyword-arg
    x = f['x']
    y = f['y']

  rng = np.random.RandomState(seed)
  indices = np.arange(len(x))
  rng.shuffle(indices)

            

Reported by Pylint.

              # ==============================================================================
"""Tests for saving and loading using keras save/load APIs with DS."""

import tensorflow.compat.v2 as tf
from keras import testing_utils
from keras.distribute import saved_model_test_base as test_base
from keras.saving import save



            

Reported by Pylint.

              
@testing_utils.run_all_without_tensor_float_32(
    'Uses Dense layers, which call matmul')
class KerasSaveLoadTest(test_base.TestSavedModelBase):

  def setUp(self):
    self._root_dir = 'keras_save_load'
    super(KerasSaveLoadTest, self).setUp()


            

Reported by Pylint.

                  'Uses Dense layers, which call matmul')
class KerasSaveLoadTest(test_base.TestSavedModelBase):

  def setUp(self):
    self._root_dir = 'keras_save_load'
    super(KerasSaveLoadTest, self).setUp()

  def _save_model(self, model, saved_dir):
    model.save(saved_dir, save_format='tf')

            

Reported by Pylint.

              class KerasSaveLoadTest(test_base.TestSavedModelBase):

  def setUp(self):
    self._root_dir = 'keras_save_load'
    super(KerasSaveLoadTest, self).setUp()

  def _save_model(self, model, saved_dir):
    model.save(saved_dir, save_format='tf')


            

Reported by Pylint.

              
  def setUp(self):
    self._root_dir = 'keras_save_load'
    super(KerasSaveLoadTest, self).setUp()

  def _save_model(self, model, saved_dir):
    model.save(saved_dir, save_format='tf')

  def _load_and_run_model(self,

            

Reported by Pylint.

              
  def setUp(self):
    self._root_dir = 'keras_save_load'
    super(KerasSaveLoadTest, self).setUp()

  def _save_model(self, model, saved_dir):
    model.save(saved_dir, save_format='tf')

  def _load_and_run_model(self,

            

Reported by Pylint.

                  self._root_dir = 'keras_save_load'
    super(KerasSaveLoadTest, self).setUp()

  def _save_model(self, model, saved_dir):
    model.save(saved_dir, save_format='tf')

  def _load_and_run_model(self,
                          distribution,
                          saved_dir,

            

Reported by Pylint.

                  super(KerasSaveLoadTest, self).setUp()

  def _save_model(self, model, saved_dir):
    model.save(saved_dir, save_format='tf')

  def _load_and_run_model(self,
                          distribution,
                          saved_dir,
                          predict_dataset,

            

Reported by Pylint.

                def _save_model(self, model, saved_dir):
    model.save(saved_dir, save_format='tf')

  def _load_and_run_model(self,
                          distribution,
                          saved_dir,
                          predict_dataset,
                          output_name='output_1'):
    restored_keras_model = save.load_model(saved_dir)

            

Reported by Pylint.

                                        saved_dir,
                          predict_dataset,
                          output_name='output_1'):
    restored_keras_model = save.load_model(saved_dir)
    return restored_keras_model.predict(
        predict_dataset, steps=test_base.PREDICT_STEPS)

  @tf.__internal__.distribute.combinations.generate(test_base.simple_models_with_strategies())
  def test_save_no_strategy_restore_strategy(self, model_and_input,

            

Reported by Pylint.

              from __future__ import division
from __future__ import print_function

import tensorflow as tf
ds_combinations = tf.__internal__.distribute.combinations

# Note: Strategy combinations are not (yet) public APIs, so they are subject
# to API changes and backward-compatibility is not guaranteed.
# TODO(b/188763034): Proceed to export the strategy combinations as public APIs.

            

Reported by Pylint.

              
# Note: Strategy combinations are not (yet) public APIs, so they are subject
# to API changes and backward-compatibility is not guaranteed.
# TODO(b/188763034): Proceed to export the strategy combinations as public APIs.
STRATEGIES = [
    ds_combinations.default_strategy,
    ds_combinations.mirrored_strategy_with_cpu_1_and_2,
    ds_combinations.mirrored_strategy_with_two_gpus,
    ds_combinations.tpu_strategy,

            

Reported by Pylint.

              @ds_combinations.generate(
    tf.__internal__.test.combinations.combine(
        strategy=STRATEGIES, mode="eager"))
class DistributedTrainingTest(tf.test.TestCase):
  """Test to demonstrate basic Keras training with a variety of strategies."""

  def testKerasTrainingAPI(self, strategy):

    # A `dataset_fn` is required for `Model.fit` to work across all strategies.

            

Reported by Pylint.

                  tf.__internal__.test.combinations.combine(
        strategy=STRATEGIES, mode="eager"))
class DistributedTrainingTest(tf.test.TestCase):
  """Test to demonstrate basic Keras training with a variety of strategies."""

  def testKerasTrainingAPI(self, strategy):

    # A `dataset_fn` is required for `Model.fit` to work across all strategies.
    def dataset_fn(input_context):

            

Reported by Pylint.

              class DistributedTrainingTest(tf.test.TestCase):
  """Test to demonstrate basic Keras training with a variety of strategies."""

  def testKerasTrainingAPI(self, strategy):

    # A `dataset_fn` is required for `Model.fit` to work across all strategies.
    def dataset_fn(input_context):
      batch_size = input_context.get_per_replica_batch_size(
          global_batch_size=64)

            

Reported by Pylint.

              class DistributedTrainingTest(tf.test.TestCase):
  """Test to demonstrate basic Keras training with a variety of strategies."""

  def testKerasTrainingAPI(self, strategy):

    # A `dataset_fn` is required for `Model.fit` to work across all strategies.
    def dataset_fn(input_context):
      batch_size = input_context.get_per_replica_batch_size(
          global_batch_size=64)

            

Reported by Pylint.

              class DistributedTrainingTest(tf.test.TestCase):
  """Test to demonstrate basic Keras training with a variety of strategies."""

  def testKerasTrainingAPI(self, strategy):

    # A `dataset_fn` is required for `Model.fit` to work across all strategies.
    def dataset_fn(input_context):
      batch_size = input_context.get_per_replica_batch_size(
          global_batch_size=64)

            

Reported by Pylint.

              class DistributedTrainingTest(tf.test.TestCase):
  """Test to demonstrate basic Keras training with a variety of strategies."""

  def testKerasTrainingAPI(self, strategy):

    # A `dataset_fn` is required for `Model.fit` to work across all strategies.
    def dataset_fn(input_context):
      batch_size = input_context.get_per_replica_batch_size(
          global_batch_size=64)

            

Reported by Pylint.

                def testKerasTrainingAPI(self, strategy):

    # A `dataset_fn` is required for `Model.fit` to work across all strategies.
    def dataset_fn(input_context):
      batch_size = input_context.get_per_replica_batch_size(
          global_batch_size=64)
      x = tf.random.uniform((10, 10))
      y = tf.random.uniform((10,))
      dataset = tf.data.Dataset.from_tensor_slices((x, y)).shuffle(10).repeat()

            

Reported by Pylint.

              
    # A `dataset_fn` is required for `Model.fit` to work across all strategies.
    def dataset_fn(input_context):
      batch_size = input_context.get_per_replica_batch_size(
          global_batch_size=64)
      x = tf.random.uniform((10, 10))
      y = tf.random.uniform((10,))
      dataset = tf.data.Dataset.from_tensor_slices((x, y)).shuffle(10).repeat()
      dataset = dataset.shard(

            

Reported by Pylint.

              # See the License for the specific language governing permissions and
# limitations under the License.
# =============================================================================
# pylint: disable=g-classes-have-attributes
"""Contains the normalization layer classes and their functional aliases."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function


            

Reported by Pylint.

              from __future__ import division
from __future__ import print_function

import tensorflow.compat.v2 as tf

import warnings

from keras.layers.normalization import batch_normalization_v1
from keras.legacy_tf_layers import base

            

Reported by Pylint.

              
from keras.layers.normalization import batch_normalization_v1
from keras.legacy_tf_layers import base
from tensorflow.python.util.tf_export import keras_export
from tensorflow.python.util.tf_export import tf_export


@keras_export(v1=['keras.__internal__.legacy.layers.BatchNormalization'])
@tf_export(v1=['layers.BatchNormalization'])

            

Reported by Pylint.

              from keras.layers.normalization import batch_normalization_v1
from keras.legacy_tf_layers import base
from tensorflow.python.util.tf_export import keras_export
from tensorflow.python.util.tf_export import tf_export


@keras_export(v1=['keras.__internal__.legacy.layers.BatchNormalization'])
@tf_export(v1=['layers.BatchNormalization'])
class BatchNormalization(batch_normalization_v1.BatchNormalization, base.Layer):

            

Reported by Pylint.

              
import tensorflow.compat.v2 as tf

import warnings

from keras.layers.normalization import batch_normalization_v1
from keras.legacy_tf_layers import base
from tensorflow.python.util.tf_export import keras_export
from tensorflow.python.util.tf_export import tf_export

            

Reported by Pylint.

              @keras_export(v1=['keras.__internal__.legacy.layers.BatchNormalization'])
@tf_export(v1=['layers.BatchNormalization'])
class BatchNormalization(batch_normalization_v1.BatchNormalization, base.Layer):
  """Batch Normalization layer from (Ioffe et al., 2015).

  Keras APIs handle BatchNormalization updates to the moving_mean and
  moving_variance as part of their `fit()` and `evaluate()` loops. However, if a
  custom training loop is used with an instance of `Model`, these updates need
  to be explicitly included.  Here's a simple example of how it can be done:

            

Reported by Pylint.

                  Batch Renormalization - Towards Reducing Minibatch Dependence in
      Batch-Normalized Models:
      [Ioffe,
        2017](http://papers.nips.cc/paper/6790-batch-renormalization-towards-reducing-minibatch-dependence-in-batch-normalized-models)
      ([pdf](http://papers.nips.cc/paper/6790-batch-renormalization-towards-reducing-minibatch-dependence-in-batch-normalized-models.pdf))


  @compatibility(TF2)
  This API is not compatible with eager execution or `tf.function`.

            

Reported by Pylint.

                    Batch-Normalized Models:
      [Ioffe,
        2017](http://papers.nips.cc/paper/6790-batch-renormalization-towards-reducing-minibatch-dependence-in-batch-normalized-models)
      ([pdf](http://papers.nips.cc/paper/6790-batch-renormalization-towards-reducing-minibatch-dependence-in-batch-normalized-models.pdf))


  @compatibility(TF2)
  This API is not compatible with eager execution or `tf.function`.


            

Reported by Pylint.

                @end_compatibility
  """

  def __init__(self,
               axis=-1,
               momentum=0.99,
               epsilon=1e-3,
               center=True,
               scale=True,

            

Reported by Pylint.

                @end_compatibility
  """

  def __init__(self,
               axis=-1,
               momentum=0.99,
               epsilon=1e-3,
               center=True,
               scale=True,

            

Reported by Pylint.

              # ==============================================================================
"""Keras estimator API."""

import tensorflow.compat.v2 as tf

from tensorflow.python.util.tf_export import keras_export

# Keras has undeclared dependency on tensorflow/estimator:estimator_py.
# As long as you depend //third_party/py/tensorflow:tensorflow target

            

Reported by Pylint.

              
import tensorflow.compat.v2 as tf

from tensorflow.python.util.tf_export import keras_export

# Keras has undeclared dependency on tensorflow/estimator:estimator_py.
# As long as you depend //third_party/py/tensorflow:tensorflow target
# everything will work as normal.


            

Reported by Pylint.

                """

  try:
    from tensorflow_estimator.python.estimator import keras_lib  # pylint: disable=g-import-not-at-top
  except ImportError:
    raise NotImplementedError(
        'tf.keras.estimator.model_to_estimator function not available in your '
        'installation.')
  _model_to_estimator_usage_gauge.get_cell('v1').set(True)

            

Reported by Pylint.

                """

  try:
    from tensorflow_estimator.python.estimator import keras_lib  # pylint: disable=g-import-not-at-top
  except ImportError:
    raise NotImplementedError(
        'tf.keras.estimator.model_to_estimator function not available in your '
        'installation.')
  _model_to_estimator_usage_gauge.get_cell('v2').set(True)

            

Reported by Pylint.

                try:
    from tensorflow_estimator.python.estimator import keras_lib  # pylint: disable=g-import-not-at-top
  except ImportError:
    raise NotImplementedError(
        'tf.keras.estimator.model_to_estimator function not available in your '
        'installation.')
  _model_to_estimator_usage_gauge.get_cell('v1').set(True)
  return keras_lib.model_to_estimator(  # pylint:disable=unexpected-keyword-arg
      keras_model=keras_model,

            

Reported by Pylint.

                try:
    from tensorflow_estimator.python.estimator import keras_lib  # pylint: disable=g-import-not-at-top
  except ImportError:
    raise NotImplementedError(
        'tf.keras.estimator.model_to_estimator function not available in your '
        'installation.')
  _model_to_estimator_usage_gauge.get_cell('v2').set(True)
  return keras_lib.model_to_estimator(  # pylint:disable=unexpected-keyword-arg
      keras_model=keras_model,

            

Reported by Pylint.

              
# LINT.IfChange
@keras_export(v1=['keras.estimator.model_to_estimator'])
def model_to_estimator(
    keras_model=None,
    keras_model_path=None,
    custom_objects=None,
    model_dir=None,
    config=None,

            

Reported by Pylint.

                  checkpoint_format='saver',
    metric_names_map=None,
    export_outputs=None):
  """Constructs an `Estimator` instance from given keras model.

  If you use infrastructure or other tooling that relies on Estimators, you can
  still build a Keras model and use model_to_estimator to convert the Keras
  model to an Estimator for use with downstream systems.


            

Reported by Pylint.

                  ValueError: If an invalid checkpoint_format was given.
  """

  try:
    from tensorflow_estimator.python.estimator import keras_lib  # pylint: disable=g-import-not-at-top
  except ImportError:
    raise NotImplementedError(
        'tf.keras.estimator.model_to_estimator function not available in your '
        'installation.')

            

Reported by Pylint.

                """

  try:
    from tensorflow_estimator.python.estimator import keras_lib  # pylint: disable=g-import-not-at-top
  except ImportError:
    raise NotImplementedError(
        'tf.keras.estimator.model_to_estimator function not available in your '
        'installation.')
  _model_to_estimator_usage_gauge.get_cell('v1').set(True)

            

Reported by Pylint.

              tf.saved_model.save().
"""

import tensorflow.compat.v2 as tf
from keras import testing_utils
from keras.distribute import saved_model_test_base as test_base
from keras.saving import save

_DEFAULT_FUNCTION_KEY = 'serving_default'

            

Reported by Pylint.

              
@testing_utils.run_all_without_tensor_float_32(
    'Uses Dense layers, which call matmul')
class SavedModelSaveAndLoadTest(test_base.TestSavedModelBase):

  def setUp(self):
    self._root_dir = 'saved_model_save_load'
    super(SavedModelSaveAndLoadTest, self).setUp()


            

Reported by Pylint.

                  'Uses Dense layers, which call matmul')
class SavedModelSaveAndLoadTest(test_base.TestSavedModelBase):

  def setUp(self):
    self._root_dir = 'saved_model_save_load'
    super(SavedModelSaveAndLoadTest, self).setUp()

  def _save_model(self, model, saved_dir):
    save.save_model(model, saved_dir, save_format='tf')

            

Reported by Pylint.

              class SavedModelSaveAndLoadTest(test_base.TestSavedModelBase):

  def setUp(self):
    self._root_dir = 'saved_model_save_load'
    super(SavedModelSaveAndLoadTest, self).setUp()

  def _save_model(self, model, saved_dir):
    save.save_model(model, saved_dir, save_format='tf')


            

Reported by Pylint.

              
  def setUp(self):
    self._root_dir = 'saved_model_save_load'
    super(SavedModelSaveAndLoadTest, self).setUp()

  def _save_model(self, model, saved_dir):
    save.save_model(model, saved_dir, save_format='tf')

  def _load_and_run_model(self,

            

Reported by Pylint.

              
  def setUp(self):
    self._root_dir = 'saved_model_save_load'
    super(SavedModelSaveAndLoadTest, self).setUp()

  def _save_model(self, model, saved_dir):
    save.save_model(model, saved_dir, save_format='tf')

  def _load_and_run_model(self,

            

Reported by Pylint.

                  self._root_dir = 'saved_model_save_load'
    super(SavedModelSaveAndLoadTest, self).setUp()

  def _save_model(self, model, saved_dir):
    save.save_model(model, saved_dir, save_format='tf')

  def _load_and_run_model(self,
                          distribution,
                          saved_dir,

            

Reported by Pylint.

                  super(SavedModelSaveAndLoadTest, self).setUp()

  def _save_model(self, model, saved_dir):
    save.save_model(model, saved_dir, save_format='tf')

  def _load_and_run_model(self,
                          distribution,
                          saved_dir,
                          predict_dataset,

            

Reported by Pylint.

                def _save_model(self, model, saved_dir):
    save.save_model(model, saved_dir, save_format='tf')

  def _load_and_run_model(self,
                          distribution,
                          saved_dir,
                          predict_dataset,
                          output_name='output_1'):
    return test_base.load_and_run_with_saved_model_api(distribution, saved_dir,

            

Reported by Pylint.

                                        saved_dir,
                          predict_dataset,
                          output_name='output_1'):
    return test_base.load_and_run_with_saved_model_api(distribution, saved_dir,
                                                       predict_dataset,
                                                       output_name)

  @tf.__internal__.distribute.combinations.generate(test_base.simple_models_with_strategies())
  def test_save_no_strategy_restore_strategy(self, model_and_input,

            

Reported by Pylint.

              # limitations under the License.
# ==============================================================================
"""Contains the RepeatVector layer."""
# pylint: disable=g-classes-have-attributes,g-direct-tensorflow-import

from keras import backend as K
from keras.engine.base_layer import Layer
from keras.engine.input_spec import InputSpec
import tensorflow.compat.v2 as tf

            

Reported by Pylint.

              # limitations under the License.
# ==============================================================================
"""Contains the RepeatVector layer."""
# pylint: disable=g-classes-have-attributes,g-direct-tensorflow-import

from keras import backend as K
from keras.engine.base_layer import Layer
from keras.engine.input_spec import InputSpec
import tensorflow.compat.v2 as tf

            

Reported by Pylint.

              from keras import backend as K
from keras.engine.base_layer import Layer
from keras.engine.input_spec import InputSpec
import tensorflow.compat.v2 as tf

from tensorflow.python.util.tf_export import keras_export


@keras_export('keras.layers.RepeatVector')

            

Reported by Pylint.

              from keras.engine.input_spec import InputSpec
import tensorflow.compat.v2 as tf

from tensorflow.python.util.tf_export import keras_export


@keras_export('keras.layers.RepeatVector')
class RepeatVector(Layer):
  """Repeats the input n times.

            

Reported by Pylint.

                  input_shape = tf.TensorShape(input_shape).as_list()
    return tf.TensorShape([input_shape[0], self.n, input_shape[1]])

  def call(self, inputs):
    return K.repeat(inputs, self.n)

  def get_config(self):
    config = {'n': self.n}
    base_config = super(RepeatVector, self).get_config()

            

Reported by Pylint.

              
@keras_export('keras.layers.RepeatVector')
class RepeatVector(Layer):
  """Repeats the input n times.

  Example:

  ```python
  model = Sequential()

            

Reported by Pylint.

                Output shape: 3D tensor of shape `(num_samples, n, features)`.
  """

  def __init__(self, n, **kwargs):
    super(RepeatVector, self).__init__(**kwargs)
    self.n = n
    if not isinstance(n, int):
      raise TypeError(f'Expected an integer value for `n`, got {type(n)}.')
    self.input_spec = InputSpec(ndim=2)

            

Reported by Pylint.

                """

  def __init__(self, n, **kwargs):
    super(RepeatVector, self).__init__(**kwargs)
    self.n = n
    if not isinstance(n, int):
      raise TypeError(f'Expected an integer value for `n`, got {type(n)}.')
    self.input_spec = InputSpec(ndim=2)


            

Reported by Pylint.

                """

  def __init__(self, n, **kwargs):
    super(RepeatVector, self).__init__(**kwargs)
    self.n = n
    if not isinstance(n, int):
      raise TypeError(f'Expected an integer value for `n`, got {type(n)}.')
    self.input_spec = InputSpec(ndim=2)


            

Reported by Pylint.

              
  def __init__(self, n, **kwargs):
    super(RepeatVector, self).__init__(**kwargs)
    self.n = n
    if not isinstance(n, int):
      raise TypeError(f'Expected an integer value for `n`, got {type(n)}.')
    self.input_spec = InputSpec(ndim=2)

  def compute_output_shape(self, input_shape):

            

Reported by Pylint.