code: def np_doc_only(np_fun_name, np_fun=None):
      """Attachs numpy docstring to a function.
  
      This differs from np_doc in that it doesn't check for a match in signature.
  
      Args:
        np_fun_name: name for the np_fun symbol. At least one of np_fun or
          np_fun_name shoud be set.
        np_fun: (optional) the numpy function whose docstring will be used.
  
      Returns:
        A function decorator that attaches the docstring from `np_fun` to the
        decorated function.
      """
      np_fun_name, np_fun = _prepare_np_fun_name_and_fun(
          np_fun_name, np_fun
      )
  
      def decorator(f):
          f.__doc__ = _np_doc_helper(f, np_fun, np_fun_name=np_fun_name)
          return f
  
      return decorator
  
imports: import inspect
  import numbers
  import os
  import re
  import numpy
  from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import flexible_dtypes
  from tensorflow.python.framework import indexed_slices
  from tensorflow.python.framework import ops
  from tensorflow.python.framework import tensor_util
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import cond
  from tensorflow.python.ops import math_ops
  from tensorflow.python.ops.numpy_ops import np_arrays
  from tensorflow.python.ops.numpy_ops import np_dtypes
  from tensorflow.python.types import core
  from tensorflow.python.util import nest
  from tensorflow.python.util import tf_export
label_desc: Use image node_id 22 for calling a global function with example usage: np_doc_only(np_fun_name, np_fun) and returns: decorator, f