old_contents: # Copyright 2017 Google Inc.
  #
  # 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.
  """Miscellaneous utility function.
  """
  
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  import tensorflow as tf
  
  
  def templatemethod(name_):
    """This decorator wraps a method with `tf.make_template`. For example,
  
    @templatemethod
    def my_method():
      # Create variables
    """
  
    def template_decorator(func):
      """Inner decorator function"""
  
      def func_wrapper(*args, **kwargs):
        """Inner wrapper function"""
        templated_func = tf.make_template(name_, func)
        return templated_func(*args, **kwargs)
  
      return func_wrapper
  
    return template_decorator
  
  
  def add_dict_to_collection(dict_, collection_name):
    """Adds a dictionary to a graph collection.
  
    Args:
      dict_: A dictionary of string keys to tensor values
      collection_name: The name of the collection to add the dictionary to
    """
    key_collection = collection_name + "_keys"
    value_collection = collection_name + "_values"
    for key, value in dict_.items():
      tf.add_to_collection(key_collection, key)
      tf.add_to_collection(value_collection, value)
  
  
  def get_dict_from_collection(collection_name):
    """Adds a dictionary to a graph collection.
  
    Args:
      collection_name: A collection name to read a dictionary from
  
    Returns:
      A dictionary with string keys and tensor values
    """
    key_collection = collection_name + "_keys"
    value_collection = collection_name + "_values"
    keys = tf.get_collection(key_collection)
    values = tf.get_collection(value_collection)
    return dict(zip(keys, values))
  
new_contents: # Copyright 2017 Google Inc.
  #
  # 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.
  """Miscellaneous utility function.
  """
  
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  import tensorflow as tf
  
  
  def templatemethod(name_):
    """This decorator wraps a method with `tf.make_template`. For example,
  
    @templatemethod
    def my_method():
      # Create variables
    """
  
    def template_decorator(func):
      """Inner decorator function"""
  
      def func_wrapper(*args, **kwargs):
        """Inner wrapper function"""
        templated_func = tf.make_template(name_, func)
        return templated_func(*args, **kwargs)
  
      return func_wrapper
  
    return template_decorator
  
  
  def add_dict_to_collection(dict_, collection_name):
    """Adds a dictionary to a graph collection.
  
    Args:
      dict_: A dictionary of string keys to tensor values
      collection_name: The name of the collection to add the dictionary to
    """
    key_collection = collection_name + "_keys"
    value_collection = collection_name + "_values"
    for key, value in dict_.items():
      tf.add_to_collection(key_collection, key)
      tf.add_to_collection(value_collection, value)
  
  
  def get_dict_from_collection(collection_name):
    """Gets a dictionary from a graph collection.
  
    Args:
      collection_name: A collection name to read a dictionary from
  
    Returns:
      A dictionary with string keys and tensor values
    """
    key_collection = collection_name + "_keys"
    value_collection = collection_name + "_values"
    keys = tf.get_collection(key_collection)
    values = tf.get_collection(value_collection)
    return dict(zip(keys, values))
  
subject: Fix a comment