code: def _wrap_decorator(wrapped_function, decorator_name):
      """Indicate that one function wraps another.
  
      This decorator wraps a function using `tf_decorator.make_decorator`
      so that doc generation scripts can pick up original function
      signature.
      It would be better to use @functools.wrap decorator, but it would
      not update function signature to match wrapped function in Python 2.
  
      Args:
        wrapped_function: The function that decorated function wraps.
        decorator_name: The name of the decorator.
  
      Returns:
        Function that accepts wrapper function as an argument and returns
        `TFDecorator` instance.
      """
  
      def wrapper(wrapper_func):
          return tf_decorator.make_decorator(
              wrapped_function, wrapper_func, decorator_name
          )
  
      return wrapper
  
imports: import collections
  import functools
  import inspect
  import re
  from tensorflow.python.framework import strict_mode
  from tensorflow.python.platform import tf_logging
  from tensorflow.python.util import decorator_utils
  from tensorflow.python.util import is_in_graph_mode
  from tensorflow.python.util import tf_contextlib
  from tensorflow.python.util import tf_decorator
  from tensorflow.python.util import tf_inspect
  from tensorflow.tools.docs import doc_controls
label_desc: Use image node_id 8 for calling a global function with example usage: _wrap_decorator(wrapped_function, decorator_name) and returns: wrapper, tf_decorator