# ==============================================================================
"""Tests for Keras weights constraints."""

import tensorflow.compat.v2 as tf

import math

import numpy as np


            

Reported by Pylint.

              
import tensorflow.compat.v2 as tf

import math

import numpy as np

from keras import backend
from keras import combinations

            

Reported by Pylint.

              from keras import constraints


def get_test_values():
  return [0.1, 0.5, 3, 8, 1e-7]


def get_example_array():
  np.random.seed(3537)

            

Reported by Pylint.

              

def get_test_values():
  return [0.1, 0.5, 3, 8, 1e-7]


def get_example_array():
  np.random.seed(3537)
  example_array = np.random.random((100, 100)) * 100. - 50.

            

Reported by Pylint.

                return [0.1, 0.5, 3, 8, 1e-7]


def get_example_array():
  np.random.seed(3537)
  example_array = np.random.random((100, 100)) * 100. - 50.
  example_array[0, 0] = 0.  # 0 could possibly cause trouble
  return example_array


            

Reported by Pylint.

              

def get_example_array():
  np.random.seed(3537)
  example_array = np.random.random((100, 100)) * 100. - 50.
  example_array[0, 0] = 0.  # 0 could possibly cause trouble
  return example_array



            

Reported by Pylint.

              
def get_example_array():
  np.random.seed(3537)
  example_array = np.random.random((100, 100)) * 100. - 50.
  example_array[0, 0] = 0.  # 0 could possibly cause trouble
  return example_array


def get_example_kernel(width):

            

Reported by Pylint.

              def get_example_array():
  np.random.seed(3537)
  example_array = np.random.random((100, 100)) * 100. - 50.
  example_array[0, 0] = 0.  # 0 could possibly cause trouble
  return example_array


def get_example_kernel(width):
  np.random.seed(3537)

            

Reported by Pylint.

                np.random.seed(3537)
  example_array = np.random.random((100, 100)) * 100. - 50.
  example_array[0, 0] = 0.  # 0 could possibly cause trouble
  return example_array


def get_example_kernel(width):
  np.random.seed(3537)
  example_array = np.random.rand(width, width, 2, 2)

            

Reported by Pylint.

                return example_array


def get_example_kernel(width):
  np.random.seed(3537)
  example_array = np.random.rand(width, width, 2, 2)
  return example_array



            

Reported by Pylint.

              # ==============================================================================
"""Tests for layer_utils."""

import tensorflow.compat.v2 as tf

import collections
import contextlib
import multiprocessing.dummy
import pickle

            

Reported by Pylint.

                      # because numpy limits the range of seeds) to ensure that an instance
        # returns the same value in different threads, but different instances
        # return different values.
        return int(np.random.RandomState(id(self) % (2 ** 31)).randint(2 ** 16))

      def get_test_property(self, _):
        """Function provided to .map for threading test."""
        return self.test_property


            

Reported by Pylint.

                  self.assertEqual(id(first_instance), first_instance.my_id)

    # Test that we can pickle and un-pickle
    second_instance = pickle.loads(pickle.dumps(first_instance))

    self.assertEqual(id(second_instance), second_instance.my_id)
    self.assertNotEqual(first_instance.my_id, second_instance.my_id)

    # Make sure de-serialized object uses the cache.

            

Reported by Bandit.

              
import tensorflow.compat.v2 as tf

import collections
import contextlib
import multiprocessing.dummy
import pickle
import time
import timeit

            

Reported by Pylint.

              import tensorflow.compat.v2 as tf

import collections
import contextlib
import multiprocessing.dummy
import pickle
import time
import timeit


            

Reported by Pylint.

              
import collections
import contextlib
import multiprocessing.dummy
import pickle
import time
import timeit

import numpy as np

            

Reported by Pylint.

              import collections
import contextlib
import multiprocessing.dummy
import pickle
import time
import timeit

import numpy as np


            

Reported by Pylint.

              import collections
import contextlib
import multiprocessing.dummy
import pickle
import time
import timeit

import numpy as np


            

Reported by Bandit.

              import contextlib
import multiprocessing.dummy
import pickle
import time
import timeit

import numpy as np

from keras.utils import layer_utils

            

Reported by Pylint.

              import multiprocessing.dummy
import pickle
import time
import timeit

import numpy as np

from keras.utils import layer_utils


            

Reported by Pylint.

              # ==============================================================================
"""Keras layers API."""

import tensorflow.compat.v2 as tf

# pylint: disable=g-bad-import-order,g-direct-tensorflow-import,disable=g-import-not-at-top
from tensorflow.python import tf2

# Generic layers.

            

Reported by Pylint.

              
import tensorflow.compat.v2 as tf

# pylint: disable=g-bad-import-order,g-direct-tensorflow-import,disable=g-import-not-at-top
from tensorflow.python import tf2

# Generic layers.
from keras.engine.input_layer import Input
from keras.engine.input_layer import InputLayer

            

Reported by Pylint.

              
import tensorflow.compat.v2 as tf

# pylint: disable=g-bad-import-order,g-direct-tensorflow-import,disable=g-import-not-at-top
from tensorflow.python import tf2

# Generic layers.
from keras.engine.input_layer import Input
from keras.engine.input_layer import InputLayer

            

Reported by Pylint.

              
import tensorflow.compat.v2 as tf

# pylint: disable=g-bad-import-order,g-direct-tensorflow-import,disable=g-import-not-at-top
from tensorflow.python import tf2

# Generic layers.
from keras.engine.input_layer import Input
from keras.engine.input_layer import InputLayer

            

Reported by Pylint.

              import tensorflow.compat.v2 as tf

# pylint: disable=g-bad-import-order,g-direct-tensorflow-import,disable=g-import-not-at-top
from tensorflow.python import tf2

# Generic layers.
from keras.engine.input_layer import Input
from keras.engine.input_layer import InputLayer
from keras.engine.input_spec import InputSpec

            

Reported by Pylint.

                  serialization.populate_deserializable_objects()
    if name in serialization.LOCAL.ALL_OBJECTS:
      return serialization.LOCAL.ALL_OBJECTS[name]
    return super(VersionAwareLayers, self).__getattr__(name)

            

Reported by Pylint.

              from keras.layers.normalization.batch_normalization import SyncBatchNormalization

if tf.__internal__.tf2.enabled():
  from keras.layers.normalization.batch_normalization import BatchNormalization
  from keras.layers.normalization.batch_normalization_v1 import BatchNormalization as BatchNormalizationV1
  BatchNormalizationV2 = BatchNormalization
else:
  from keras.layers.normalization.batch_normalization_v1 import BatchNormalization
  from keras.layers.normalization.batch_normalization import BatchNormalization as BatchNormalizationV2

            

Reported by Pylint.

              
if tf.__internal__.tf2.enabled():
  from keras.layers.normalization.batch_normalization import BatchNormalization
  from keras.layers.normalization.batch_normalization_v1 import BatchNormalization as BatchNormalizationV1
  BatchNormalizationV2 = BatchNormalization
else:
  from keras.layers.normalization.batch_normalization_v1 import BatchNormalization
  from keras.layers.normalization.batch_normalization import BatchNormalization as BatchNormalizationV2
  BatchNormalizationV1 = BatchNormalization

            

Reported by Pylint.

              
if tf.__internal__.tf2.enabled():
  from keras.layers.normalization.batch_normalization import BatchNormalization
  from keras.layers.normalization.batch_normalization_v1 import BatchNormalization as BatchNormalizationV1
  BatchNormalizationV2 = BatchNormalization
else:
  from keras.layers.normalization.batch_normalization_v1 import BatchNormalization
  from keras.layers.normalization.batch_normalization import BatchNormalization as BatchNormalizationV2
  BatchNormalizationV1 = BatchNormalization

            

Reported by Pylint.

              if tf.__internal__.tf2.enabled():
  from keras.layers.normalization.batch_normalization import BatchNormalization
  from keras.layers.normalization.batch_normalization_v1 import BatchNormalization as BatchNormalizationV1
  BatchNormalizationV2 = BatchNormalization
else:
  from keras.layers.normalization.batch_normalization_v1 import BatchNormalization
  from keras.layers.normalization.batch_normalization import BatchNormalization as BatchNormalizationV2
  BatchNormalizationV1 = BatchNormalization


            

Reported by Pylint.

              # ==============================================================================
"""Tests for dataset_creator."""

import tensorflow.compat.v2 as tf

from absl.testing import parameterized
from tensorflow.python.distribute.cluster_resolver import SimpleClusterResolver
from keras import combinations
from keras.distribute import multi_worker_testing_utils

            

Reported by Pylint.

              
import tensorflow.compat.v2 as tf

from absl.testing import parameterized
from tensorflow.python.distribute.cluster_resolver import SimpleClusterResolver
from keras import combinations
from keras.distribute import multi_worker_testing_utils
from keras.engine import data_adapter
from keras.engine import sequential

            

Reported by Pylint.

              import tensorflow.compat.v2 as tf

from absl.testing import parameterized
from tensorflow.python.distribute.cluster_resolver import SimpleClusterResolver
from keras import combinations
from keras.distribute import multi_worker_testing_utils
from keras.engine import data_adapter
from keras.engine import sequential
from keras.layers import core as core_layers

            

Reported by Pylint.

              from keras.layers import core as core_layers
from keras.optimizer_v2 import gradient_descent
from keras.utils import dataset_creator
from tensorflow.python.training.server_lib import ClusterSpec


class DatasetCreatorTest(tf.test.TestCase, parameterized.TestCase):

  def test_dataset_creator(self):

            

Reported by Pylint.

              
    # Ensuring the resulting `DistributedDatasetsFromFunction` has the right
    # options.
    self.assertTrue(data_handler._dataset._options.experimental_fetch_to_device)
    self.assertEqual(
        data_handler._dataset._options.experimental_per_replica_buffer_size, 2)

  def test_dataset_creator_input_options_with_cluster_coordinator(self):
    dataset_fn = lambda _: tf.data.Dataset.from_tensor_slices([1, 1])

            

Reported by Pylint.

              
    # Ensuring the resulting `DistributedDatasetsFromFunction` has the right
    # options.
    self.assertTrue(data_handler._dataset._options.experimental_fetch_to_device)
    self.assertEqual(
        data_handler._dataset._options.experimental_per_replica_buffer_size, 2)

  def test_dataset_creator_input_options_with_cluster_coordinator(self):
    dataset_fn = lambda _: tf.data.Dataset.from_tensor_slices([1, 1])

            

Reported by Pylint.

                  # options.
    self.assertTrue(data_handler._dataset._options.experimental_fetch_to_device)
    self.assertEqual(
        data_handler._dataset._options.experimental_per_replica_buffer_size, 2)

  def test_dataset_creator_input_options_with_cluster_coordinator(self):
    dataset_fn = lambda _: tf.data.Dataset.from_tensor_slices([1, 1])
    input_options = tf.distribute.InputOptions(
        experimental_fetch_to_device=True,

            

Reported by Pylint.

                  # options.
    self.assertTrue(data_handler._dataset._options.experimental_fetch_to_device)
    self.assertEqual(
        data_handler._dataset._options.experimental_per_replica_buffer_size, 2)

  def test_dataset_creator_input_options_with_cluster_coordinator(self):
    dataset_fn = lambda _: tf.data.Dataset.from_tensor_slices([1, 1])
    input_options = tf.distribute.InputOptions(
        experimental_fetch_to_device=True,

            

Reported by Pylint.

                  strategy = self._get_parameter_server_strategy()
    with strategy.scope():
      model = sequential.Sequential([core_layers.Dense(10)])
      model._cluster_coordinator = tf.distribute.experimental.coordinator.ClusterCoordinator(
          strategy)
      data_handler = data_adapter.get_data_handler(
          x, steps_per_epoch=2, model=model)

    iter_rv = iter(data_handler._dataset)._values[0]

            

Reported by Pylint.

                    data_handler = data_adapter.get_data_handler(
          x, steps_per_epoch=2, model=model)

    iter_rv = iter(data_handler._dataset)._values[0]
    iter_rv._rebuild_on(model._cluster_coordinator._cluster.workers[0])
    distributed_iterator = iter_rv._get_values()

    # Ensuring the resulting `DistributedIterator` has the right options.
    self.assertTrue(distributed_iterator._options.experimental_fetch_to_device)

            

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 convolutional 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 import layers as keras_layers
from keras.legacy_tf_layers import base

            

Reported by Pylint.

              
from keras import layers as keras_layers
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.Conv1D'])
@tf_export(v1=['layers.Conv1D'])

            

Reported by Pylint.

              from keras import layers as keras_layers
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.Conv1D'])
@tf_export(v1=['layers.Conv1D'])
class Conv1D(keras_layers.Conv1D, base.Layer):

            

Reported by Pylint.

              # Copyright 2015 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.

              
import tensorflow.compat.v2 as tf

import warnings

from keras import layers as keras_layers
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.Conv1D'])
@tf_export(v1=['layers.Conv1D'])
class Conv1D(keras_layers.Conv1D, base.Layer):
  """1D convolution layer (e.g. temporal convolution).

  This layer creates a convolution kernel that is convolved
  (actually cross-correlated) with the layer input to produce a tensor of
  outputs. If `use_bias` is True (and a `bias_initializer` is provided),
  a bias vector is created and added to the outputs. Finally, if

            

Reported by Pylint.

                @end_compatibility
  """

  def __init__(self, filters,
               kernel_size,
               strides=1,
               padding='valid',
               data_format='channels_last',
               dilation_rate=1,

            

Reported by Pylint.

                @end_compatibility
  """

  def __init__(self, filters,
               kernel_size,
               strides=1,
               padding='valid',
               data_format='channels_last',
               dilation_rate=1,

            

Reported by Pylint.

                @end_compatibility
  """

  def __init__(self, filters,
               kernel_size,
               strides=1,
               padding='valid',
               data_format='channels_last',
               dilation_rate=1,

            

Reported by Pylint.

              # ==============================================================================
"""Benchmark suite for KPL and feature column implementations."""

import tensorflow as tf
import itertools
import math
import random
import string
import time

            

Reported by Pylint.

              
import numpy as np

import keras

# This is required as of 3/2021 because otherwise we drop into graph mode.
tf.compat.v1.enable_v2_behavior()



            

Reported by Pylint.

                    self.steps += 1

  def on_predict_end(self, _):
    self.tn = time.time()
    self.t_avg = (self.tn - self.t0) / self.steps


def create_data(length, num_entries, max_value, dtype):
  """Create a ragged tensor with random data entries."""

            

Reported by Pylint.

              
  def on_predict_end(self, _):
    self.tn = time.time()
    self.t_avg = (self.tn - self.t0) / self.steps


def create_data(length, num_entries, max_value, dtype):
  """Create a ragged tensor with random data entries."""
  lengths = (np.random.random(size=num_entries) * length).astype(int)

            

Reported by Pylint.

              """Benchmark suite for KPL and feature column implementations."""

import tensorflow as tf
import itertools
import math
import random
import string
import time


            

Reported by Pylint.

              
import tensorflow as tf
import itertools
import math
import random
import string
import time

import numpy as np

            

Reported by Pylint.

              import tensorflow as tf
import itertools
import math
import random
import string
import time

import numpy as np


            

Reported by Pylint.

              import itertools
import math
import random
import string
import time

import numpy as np

import keras

            

Reported by Pylint.

              import math
import random
import string
import time

import numpy as np

import keras


            

Reported by Pylint.

              tf.compat.v1.enable_v2_behavior()


class LayerBenchmark(tf.test.Benchmark):
  """Benchmark the layer forward pass."""

  def report(self, name, keras_time, fc_time, iters):
    """Calculate and report benchmark statistics."""
    extras = {

            

Reported by Pylint.

              # ==============================================================================
"""Preprocessing stage."""

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.engine import functional

            

Reported by Pylint.

              """Preprocessing stage."""

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.engine import functional
from keras.engine import sequential

            

Reported by Pylint.

              

# Sequential methods should take precedence.
class PreprocessingStage(sequential.Sequential,
                         base_preprocessing_layer.PreprocessingLayer):
  """A sequential preprocessing stage.

  This preprocessing stage wraps a list of preprocessing layers into a
  Sequential-like object that enables you to `adapt()` the whole list via

            

Reported by Pylint.

              

# Sequential methods should take precedence.
class PreprocessingStage(sequential.Sequential,
                         base_preprocessing_layer.PreprocessingLayer):
  """A sequential preprocessing stage.

  This preprocessing stage wraps a list of preprocessing layers into a
  Sequential-like object that enables you to `adapt()` the whole list via

            

Reported by Pylint.

                  name: String. Optional name for the preprocessing stage object.
  """

  def adapt(self, data, reset_state=True):
    """Adapt the state of the layers of the preprocessing stage to the data.

    Args:
      data: A batched Dataset object, or a NumPy array, or an EagerTensor.
        Data to be iterated over to adapt the state of the layers in this

            

Reported by Pylint.

              

# Functional methods should take precedence.
class FunctionalPreprocessingStage(functional.Functional,
                                   base_preprocessing_layer.PreprocessingLayer):
  """A functional preprocessing stage.

  This preprocessing stage wraps a graph of preprocessing layers into a
  Functional-like object that enables you to `adapt()` the whole graph via

            

Reported by Pylint.

              

# Functional methods should take precedence.
class FunctionalPreprocessingStage(functional.Functional,
                                   base_preprocessing_layer.PreprocessingLayer):
  """A functional preprocessing stage.

  This preprocessing stage wraps a graph of preprocessing layers into a
  Functional-like object that enables you to `adapt()` the whole graph via

            

Reported by Pylint.

                      '`fit`. Instead, you may feed data to `adapt` the stage to set '
        'appropriate states of the layers in the stage.')

  def adapt(self, data, reset_state=True):
    """Adapt the state of the layers of the preprocessing stage to the data.

    Args:
      data: A batched Dataset object, a NumPy array, an EagerTensor, or a list,
        dict or nested structure of Numpy Arrays or EagerTensors. The elements

            

Reported by Pylint.

              

# Sequential methods should take precedence.
class PreprocessingStage(sequential.Sequential,
                         base_preprocessing_layer.PreprocessingLayer):
  """A sequential preprocessing stage.

  This preprocessing stage wraps a list of preprocessing layers into a
  Sequential-like object that enables you to `adapt()` the whole list via

            

Reported by Pylint.

              # Sequential methods should take precedence.
class PreprocessingStage(sequential.Sequential,
                         base_preprocessing_layer.PreprocessingLayer):
  """A sequential preprocessing stage.

  This preprocessing stage wraps a list of preprocessing layers into a
  Sequential-like object that enables you to `adapt()` the whole list via
  a single `adapt()` call on the preprocessing stage.


            

Reported by Pylint.

              # ==============================================================================
"""Tests for numerical correctness."""

import tensorflow.compat.v2 as tf

from absl.testing import parameterized
import numpy as np

import keras

            

Reported by Pylint.

              
import tensorflow.compat.v2 as tf

from absl.testing import parameterized
import numpy as np

import keras
from keras import keras_parameterized
from keras import testing_utils

            

Reported by Pylint.

              from keras import testing_utils


class MultiInputSubclassed(keras.Model):
  """Subclassed Model that adds its inputs and then adds a bias."""

  def __init__(self):
    super(MultiInputSubclassed, self).__init__()
    self.add = keras.layers.Add()

            

Reported by Pylint.

                  self.add = keras.layers.Add()
    self.bias = testing_utils.Bias()

  def call(self, inputs):
    added = self.add(inputs)
    return self.bias(added)


def multi_input_functional():

            

Reported by Pylint.

              

class MultiInputSubclassed(keras.Model):
  """Subclassed Model that adds its inputs and then adds a bias."""

  def __init__(self):
    super(MultiInputSubclassed, self).__init__()
    self.add = keras.layers.Add()
    self.bias = testing_utils.Bias()

            

Reported by Pylint.

              class MultiInputSubclassed(keras.Model):
  """Subclassed Model that adds its inputs and then adds a bias."""

  def __init__(self):
    super(MultiInputSubclassed, self).__init__()
    self.add = keras.layers.Add()
    self.bias = testing_utils.Bias()

  def call(self, inputs):

            

Reported by Pylint.

                """Subclassed Model that adds its inputs and then adds a bias."""

  def __init__(self):
    super(MultiInputSubclassed, self).__init__()
    self.add = keras.layers.Add()
    self.bias = testing_utils.Bias()

  def call(self, inputs):
    added = self.add(inputs)

            

Reported by Pylint.

                """Subclassed Model that adds its inputs and then adds a bias."""

  def __init__(self):
    super(MultiInputSubclassed, self).__init__()
    self.add = keras.layers.Add()
    self.bias = testing_utils.Bias()

  def call(self, inputs):
    added = self.add(inputs)

            

Reported by Pylint.

              
  def __init__(self):
    super(MultiInputSubclassed, self).__init__()
    self.add = keras.layers.Add()
    self.bias = testing_utils.Bias()

  def call(self, inputs):
    added = self.add(inputs)
    return self.bias(added)

            

Reported by Pylint.

                def __init__(self):
    super(MultiInputSubclassed, self).__init__()
    self.add = keras.layers.Add()
    self.bias = testing_utils.Bias()

  def call(self, inputs):
    added = self.add(inputs)
    return self.bias(added)


            

Reported by Pylint.

              # ==============================================================================
"""Tests for convolutional transpose layers."""

import tensorflow.compat.v2 as tf

from absl.testing import parameterized
import numpy as np

import keras

            

Reported by Pylint.

              
import tensorflow.compat.v2 as tf

from absl.testing import parameterized
import numpy as np

import keras
from keras import keras_parameterized
from keras import testing_utils

            

Reported by Pylint.

                    ('padding_same', {'padding': 'same'}),
      ('strides', {'strides': (2, 2)}),
      # Only runs on GPU with CUDA, channels_first is not supported on CPU.
      # TODO(b/62340061): Support channels_first on CPU.
      ('data_format', {'data_format': 'channels_first'}),
      ('strides_output_padding', {'strides': (2, 2), 'output_padding': (1, 1)}),
  )
  def test_conv2d_transpose(self, kwargs):
    kwargs['filters'] = 2

            

Reported by Pylint.

                    ('padding_same', {'padding': 'same'}),
      ('strides', {'strides': (2, 2, 2)}),
      # Only runs on GPU with CUDA, channels_first is not supported on CPU.
      # TODO(b/62340061): Support channels_first on CPU.
      ('data_format', {'data_format': 'channels_first'}),
      ('strides_output_padding', {'strides': (2, 2, 2),
                                  'output_padding': (1, 1, 1)}),
  )
  def test_conv3d_transpose(self, kwargs):

            

Reported by Pylint.

              

@keras_parameterized.run_all_keras_modes
class Conv2DTransposeTest(keras_parameterized.TestCase):

  def _run_test(self, kwargs):
    num_samples = 2
    stack_size = 3
    num_row = 7

            

Reported by Pylint.

              @keras_parameterized.run_all_keras_modes
class Conv2DTransposeTest(keras_parameterized.TestCase):

  def _run_test(self, kwargs):
    num_samples = 2
    stack_size = 3
    num_row = 7
    num_col = 6


            

Reported by Pylint.

              class Conv2DTransposeTest(keras_parameterized.TestCase):

  def _run_test(self, kwargs):
    num_samples = 2
    stack_size = 3
    num_row = 7
    num_col = 6

    with self.cached_session():

            

Reported by Pylint.

              
  def _run_test(self, kwargs):
    num_samples = 2
    stack_size = 3
    num_row = 7
    num_col = 6

    with self.cached_session():
      testing_utils.layer_test(

            

Reported by Pylint.

                def _run_test(self, kwargs):
    num_samples = 2
    stack_size = 3
    num_row = 7
    num_col = 6

    with self.cached_session():
      testing_utils.layer_test(
          keras.layers.Conv2DTranspose,

            

Reported by Pylint.

                  num_samples = 2
    stack_size = 3
    num_row = 7
    num_col = 6

    with self.cached_session():
      testing_utils.layer_test(
          keras.layers.Conv2DTranspose,
          kwargs=kwargs,

            

Reported by Pylint.

              # ==============================================================================
"""Tests for tensorflow.python.client.graph_util."""

import tensorflow.compat.v2 as tf

import numpy as np
from tensorflow.core.protobuf import meta_graph_pb2
import keras
from tensorflow.python.grappler import tf_optimizer

            

Reported by Pylint.

              import tensorflow.compat.v2 as tf

import numpy as np
from tensorflow.core.protobuf import meta_graph_pb2
import keras
from tensorflow.python.grappler import tf_optimizer
from tensorflow.python.training.saver import export_meta_graph



            

Reported by Pylint.

              
import numpy as np
from tensorflow.core.protobuf import meta_graph_pb2
import keras
from tensorflow.python.grappler import tf_optimizer
from tensorflow.python.training.saver import export_meta_graph


class ConvertVariablesToConstantsTest(tf.test.TestCase):

            

Reported by Pylint.

              import numpy as np
from tensorflow.core.protobuf import meta_graph_pb2
import keras
from tensorflow.python.grappler import tf_optimizer
from tensorflow.python.training.saver import export_meta_graph


class ConvertVariablesToConstantsTest(tf.test.TestCase):


            

Reported by Pylint.

              from tensorflow.core.protobuf import meta_graph_pb2
import keras
from tensorflow.python.grappler import tf_optimizer
from tensorflow.python.training.saver import export_meta_graph


class ConvertVariablesToConstantsTest(tf.test.TestCase):

  def _get_tensors(self, sess, tensor_list):

            

Reported by Pylint.

              import numpy as np
from tensorflow.core.protobuf import meta_graph_pb2
import keras
from tensorflow.python.grappler import tf_optimizer
from tensorflow.python.training.saver import export_meta_graph


class ConvertVariablesToConstantsTest(tf.test.TestCase):


            

Reported by Pylint.

              from tensorflow.python.training.saver import export_meta_graph


class ConvertVariablesToConstantsTest(tf.test.TestCase):

  def _get_tensors(self, sess, tensor_list):
    """Returns a list of Tensor objects from the Session."""
    return [
        sess.graph.get_tensor_by_name(tensor.name) for tensor in tensor_list

            

Reported by Pylint.

              
class ConvertVariablesToConstantsTest(tf.test.TestCase):

  def _get_tensors(self, sess, tensor_list):
    """Returns a list of Tensor objects from the Session."""
    return [
        sess.graph.get_tensor_by_name(tensor.name) for tensor in tensor_list
    ]


            

Reported by Pylint.

              
class ConvertVariablesToConstantsTest(tf.test.TestCase):

  def _get_tensors(self, sess, tensor_list):
    """Returns a list of Tensor objects from the Session."""
    return [
        sess.graph.get_tensor_by_name(tensor.name) for tensor in tensor_list
    ]


            

Reported by Pylint.

              class ConvertVariablesToConstantsTest(tf.test.TestCase):

  def _get_tensors(self, sess, tensor_list):
    """Returns a list of Tensor objects from the Session."""
    return [
        sess.graph.get_tensor_by_name(tensor.name) for tensor in tensor_list
    ]

  def _get_tensor_names(self, tensors):

            

Reported by Pylint.