code: def _use_grad_for_differentiable(func):
      def _use_grad(self, *args, **kwargs):
          import torch._dynamo
  
          prev_grad = torch.is_grad_enabled()
          try:
              # Note on graph break below:
              # we need to graph break to ensure that aot respects the no_grad annotation.
              # This is important for perf because without this, functionalization will generate an epilogue
              # which updates the mutated parameters of the optimizer which is *not* visible to inductor, as a result,
              # inductor will allocate for every parameter in the model, which is horrible.
              # With this, aot correctly sees that this is an inference graph, and functionalization will generate
              # an epilogue which is appended to the graph, which *is* visible to inductor, as a result, inductor sees that
              # step is in place and is able to avoid the extra allocation.
              # In the future, we will either 1) continue to graph break on backward, so this graph break does not matter
              # or 2) have a fully fused forward and backward graph, which will have no_grad by default, and we can remove this
              # graph break to allow the fully fused fwd-bwd-optimizer graph to be compiled.
              # see https://github.com/pytorch/pytorch/issues/104053
              torch.set_grad_enabled(self.defaults["differentiable"])
              torch._dynamo.graph_break()
              ret = func(self, *args, **kwargs)
          finally:
              torch._dynamo.graph_break()
              torch.set_grad_enabled(prev_grad)
          return ret
  
      functools.update_wrapper(_use_grad, func)
      return _use_grad
  
imports: import math
  import functools
  import warnings
  from collections import OrderedDict, defaultdict
  from copy import deepcopy
  from itertools import chain
  from typing import Any, Callable, DefaultDict, Dict, Hashable, Iterable, List, Optional, Set, Tuple, TypeVar, Union, cast, overload
  from typing_extensions import ParamSpec, Self, TypeAlias
  import torch
  import torch.utils.hooks
  from torch.utils.hooks import RemovableHandle
  from torch.utils._foreach_utils import Indices, TensorListList, _get_foreach_kernels_supported_devices, _get_fused_kernels_supported_devices
  from torch._utils import is_compiling
  from torch.utils._foreach_utils import _group_tensors_by_device_and_dtype
label_desc: Use image node_id 1 for calling a global function with example usage: _use_grad_for_differentiable(func) and returns: _use_grad, ret