text: # 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
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  # ==============================================================================
  """Ops for building neural network layers, regularizers, summaries, etc.
  
  ## Higher level ops for building neural network layers.
  
  This package provides several ops that take care of creating variables that are
  used internally in a consistent way and provide the building blocks for many
  common machine learning algorithms.
  
  @@avg_pool2d
  @@batch_norm
  @@convolution2d
  @@convolution2d_in_plane
  @@convolution2d_transpose
  @@flatten
  @@fully_connected
  @@layer_norm
  @@max_pool2d
  @@one_hot_encoding
  @@repeat
  @@safe_embedding_lookup_sparse
  @@separable_convolution2d
  @@stack
  @@unit_norm
  
  Aliases for fully_connected which set a default activation function are
  available: `relu`, `relu6` and `linear`.
  
  ## Regularizers
  
  Regularization can help prevent overfitting. These have the signature
  `fn(weights)`. The loss is typically added to
  `tf.GraphKeys.REGULARIZATION_LOSSES`.
  
  @@apply_regularization
  @@l1_regularizer
  @@l2_regularizer
  @@sum_regularizer
  
  ## Initializers
  
  Initializers are used to initialize variables with sensible values given their
  size, data type, and purpose.
  
  @@xavier_initializer
  @@xavier_initializer_conv2d
  @@variance_scaling_initializer
  
  ## Optimization
  
  Optimize weights given a loss.
  
  @@optimize_loss
  
  ## Summaries
  
  Helper functions to summarize specific variables or ops.
  
  @@summarize_activation
  @@summarize_tensor
  @@summarize_tensors
  @@summarize_collection
  
  The layers module defines convenience functions `summarize_variables`,
  `summarize_weights` and `summarize_biases`, which set the `collection` argument
  of `summarize_collection` to `VARIABLES`, `WEIGHTS` and `BIASES`, respectively.
  
  @@summarize_activations
  
  ## Feature columns
  
  Feature columns provide a mechanism to map data to a model.
  
  @@bucketized_column
  @@check_feature_columns
  @@create_feature_spec_for_parsing
  @@crossed_column
  @@embedding_column
  @@scattered_embedding_column
  @@input_from_feature_columns
  @@joint_weighted_sum_from_feature_columns
  @@make_place_holder_tensors_for_base_features
  @@one_hot_column
  @@parse_feature_columns_from_examples
  @@parse_feature_columns_from_sequence_examples
  @@real_valued_column
  @@shared_embedding_columns
  @@sparse_column_with_hash_bucket
  @@sparse_column_with_integerized_feature
  @@sparse_column_with_keys
  @@weighted_sparse_column
  @@weighted_sum_from_feature_columns
  
  """
  
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  import sys
  
  # pylint: disable=unused-import,wildcard-import
  from tensorflow.contrib.layers.python.layers import *
  from tensorflow.contrib.layers.python.ops import sparse_ops
  from tensorflow.python.util.all_util import make_all
  # pylint: enable=unused-import,wildcard-import
  
  __all__ = make_all(__name__)
  