code: def add_arg_scope(func):
      """Decorates a function with args so it can be used within an arg_scope.
  
      Args:
        func: function to decorate.
  
      Returns:
        A tuple with the decorated function func_with_args().
      """
  
      def func_with_args(*args, **kwargs):
          current_scope = current_arg_scope()
          current_args = kwargs
          key_func = arg_scope_func_key(func)
          if key_func in current_scope:
              current_args = current_scope[key_func].copy()
              current_args.update(kwargs)
          return func(*args, **current_args)
  
      _add_op(func)
      setattr(func_with_args, "_key_op", arg_scope_func_key(func))
      return tf_decorator.make_decorator(func, func_with_args)
  
file_path: tflearn/tflearn/vendor/arg_scope.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from tensorflow.python.util import tf_contextlib
  from tensorflow.python.util import tf_decorator
label_desc: Use image node_id 8 for calling a global function with example usage: add_arg_scope(func) and returns: tf_decorator, func