code: def traceable(fn_cls):
      r"""Mark Function as traceable for the JIT.
  
      Traceable functions have additional restrictions - they can't pass any
      data-dependent values to backward (e.g. Prod passes the output, which makes
      it non-traceable), and their backward should be implemented entirely in terms
      of operations on autograd Tensors in all cases.
  
      DON'T USE THIS DECORATOR. IT IS FOR INTERNAL USE ONLY AND SHOULD BE HANDLED WITH
      CARE (or can give incorrect results otherwise).
      """
      fn_cls.is_traceable = True
      return fn_cls
  
imports: import functools
  import inspect
  import itertools
  import warnings
  from collections import OrderedDict
  from typing import Any, List, Optional, Tuple
  import torch
  import torch._C
  import torch._functorch
  import torch.utils.hooks
  from torch._C import _functions
  from torch._functorch.autograd_function import custom_function_call
label_desc: Use image node_id 2 for calling a global function with example usage: traceable(fn_cls) and returns: fn_cls