def layer_register(
        log_shape=False,
        use_scope=True):
    """
    Args:
        log_shape (bool): log input/output shape of this layer
        use_scope (bool or None):
            Whether to call this layer with an extra first argument as variable scope.
            When set to None, it can be called either with or without
            the scope name argument, depend on whether the first argument
            is string or not.

    Returns:
        A decorator used to register a layer.

    Example:

    .. code-block:: python

        @layer_register(use_scope=True)
        def add10(x):
            return x + tf.get_variable('W', shape=[10])

        # use it:
        output = add10('layer_name', input)  # the function will be called under variable scope "layer_name".
    """

    def wrapper(func):
        @wraps(func)
        def wrapped_func(*args, **kwargs):
            assert args[0] is not None, args
            if use_scope:
                name, inputs = args[0], args[1]
                args = args[1:]  # actual positional args used to call func
                assert isinstance(name, six.string_types), "First argument for \"{}\" should be a string. ".format(
                    func.__name__) + "Did you forget to specify the name of the layer?"
            else:
                assert not log_shape
                if isinstance(args[0], six.string_types):
                    if use_scope is False:
                        logger.warn(
                            "Please call layer {} without the first scope name argument, "
                            "or register the layer with use_scope=None to allow "
                            "two calling methods.".format(func.__name__))
                    name, inputs = args[0], args[1]
                    args = args[1:]  # actual positional args used to call func
                else:
                    inputs = args[0]
                    name = None
            if not (isinstance(inputs, (tf.Tensor, tf.Variable)) or
                    (isinstance(inputs, (list, tuple)) and
                        isinstance(inputs[0], (tf.Tensor, tf.Variable)))):
                raise ValueError("Invalid inputs to layer: " + str(inputs))

            # use kwargs from current argument scope
            actual_args = copy.copy(get_arg_scope()[func.__name__])
            # explicit kwargs overwrite argscope
            actual_args.update(kwargs)
            # if six.PY3:
            #     # explicit positional args also override argscope. only work in PY3
            #     posargmap = inspect.signature(func).bind_partial(*args).arguments
            #     for k in six.iterkeys(posargmap):
            #         if k in actual_args:
            #             del actual_args[k]

            if name is not None:        # use scope
                with tfv1.variable_scope(name) as scope:
                    # this name is only used to surpress logging, doesn't hurt to do some heuristics
                    scope_name = re.sub('tower[0-9]+/', '', scope.name)
                    do_log_shape = log_shape and scope_name not in _LAYER_LOGGED
                    if do_log_shape:
                        logger.info("{} input: {}".format(scope.name, get_shape_str(inputs)))

                    # run the actual function
                    outputs = func(*args, **actual_args)

                    if do_log_shape:
                        # log shape info and add activation
                        logger.info("{} output: {}".format(
                            scope.name, get_shape_str(outputs)))
                        _LAYER_LOGGED.add(scope_name)
            else:
                # run the actual function
                outputs = func(*args, **actual_args)
            return outputs

        wrapped_func.symbolic_function = func   # attribute to access the underlying function object
        wrapped_func.use_scope = use_scope
        _register(func.__name__, wrapped_func)
        return wrapped_func

    return wrapper
Args:
        log_shape (bool): log input/output shape of this layer
        use_scope (bool or None):
            Whether to call this layer with an extra first argument as variable scope.
            When set to None, it can be called either with or without
            the scope name argument, depend on whether the first argument
            is string or not.

    Returns:
        A decorator used to register a layer.

    Example:

    .. code-block:: python

        @layer_register(use_scope=True)
        def add10(x):
            return x + tf.get_variable('W', shape=[10])

        # use it:
        output = add10('layer_name', input)  # the function will be called under variable scope "layer_name".