class_docstr: Class for custom operators in PyTorch.
  
  Use the CustomOp API to create user-defined custom operators that behave
  just like regular PyTorch operators (e.g. torch.sin, torch.mm) when it
  comes to various PyTorch subsystems (like torch.compile).
  
  To construct a `CustomOp`, use `custom_op`.
docstr: Register a function that tells us what to save for backward.
  
  Please see impl_backward for more details.
docstr_tok: ["Register","a","function","that","tells","us","what","to","save","for","backward",".","Please","see","impl_backward","for","more","details","."]
code: def impl_save_for_backward(self, _stacklevel=2):
      r"""Register a function that tells us what to save for backward.
  
      Please see impl_backward for more details.
      """
  
      def inner(f):
          self._check_can_register_backward()
          self._check_doesnt_have_library_autograd_impl()
          if not self._registered_autograd_kernel_indirection:
              self._register_autograd_kernel_indirection()
          self._register_impl(
              "save_for_backward", f, stacklevel=_stacklevel
          )
          if self._has_impl("backward"):
              self._register_autograd_kernel()
  
      return inner
  
imports: import dataclasses
  import functools
  import inspect
  import sys
  import typing
  import weakref
  from torchgen.model import FunctionSchema, OperatorName, SchemaKind, BaseType, ListType, BaseTy
  import torch
  import torch._C
  import torch.library
  from torch._library.abstract_impl import AbstractImplCtx
  from torch.library import get_ctx
  from .autograd import autograd_kernel_indirection, construct_autograd_kernel
label_desc: Use image node_id 17 for calling the CustomOp obj's underlying member method code with example usage: obj.impl_save_for_backward(_stacklevel) and returns: inner