original_docstring: 
      A decorator for functions that define TensorFlow operations. The wrapped
      function will only be executed once. Subsequent calls to it will directly
      return the result so that operations are added to the graph only once.
      The operations added by the function live within a tf.compat.v1.variable_scope(). If
      this decorator is used with arguments, they will be forwarded to the
      variable scope. The scope name defaults to the name of the wrapped
      function.
      
docstring: A decorator for functions that define TensorFlow operations. The wrapped
  function will only be executed once. Subsequent calls to it will directly
  return the result so that operations are added to the graph only once.
  The operations added by the function live within a tf.compat.v1.variable_scope(). If
  this decorator is used with arguments, they will be forwarded to the
  variable scope. The scope name defaults to the name of the wrapped
  function.
docstring_tokens: A decorator for functions that define TensorFlow operations . The wrapped function will only be executed once . Subsequent calls to it will directly return the result so that operations are added to the graph only once . The operations added by the function live within a tf . compat . v1 . variable_scope () . If this decorator is used with arguments they will be forwarded to the variable scope . The scope name defaults to the name of the wrapped function .
code: def define_scope(function, scope=None, *args, **kwargs):
      """
      A decorator for functions that define TensorFlow operations. The wrapped
      function will only be executed once. Subsequent calls to it will directly
      return the result so that operations are added to the graph only once.
      The operations added by the function live within a tf.compat.v1.variable_scope(). If
      this decorator is used with arguments, they will be forwarded to the
      variable scope. The scope name defaults to the name of the wrapped
      function.
      """
      attribute = '_cache_' + function.__name__
      name = scope or function.__name__
  
      @property
      @functools.wraps(function)
      def decorator(self):
          if not hasattr(self, attribute):
              with tf.compat.v1.variable_scope(name, *args, **kwargs):
                  setattr(self, attribute, function(self))
          return getattr(self, attribute)
  
      return decorator
code_tokens: def define_scope ( function , scope = None , * args , ** kwargs ) : attribute = '_cache_' + function . __name__ name = scope or function . __name__ @ property @ functools . wraps ( function ) def decorator ( self ) : if not hasattr ( self , attribute ) : with tf . compat . v1 . variable_scope ( name , * args , ** kwargs ) : setattr ( self , attribute , function ( self ) ) return getattr ( self , attribute ) return decorator
short_docstring: A decorator for functions that define TensorFlow operations.
short_docstring_tokens: A decorator for functions that define TensorFlow operations .
comment: """
      A decorator for functions that define TensorFlow operations. The wrapped
      function will only be executed once. Subsequent calls to it will directly
      return the result so that operations are added to the graph only once.
      The operations added by the function live within a tf.compat.v1.variable_scope(). If
      this decorator is used with arguments, they will be forwarded to the
      variable scope. The scope name defaults to the name of the wrapped
      function.
      """