code: def test_autocast_flash_attention(self):
      def fn(primals_1, primals_2, primals_3):
          return torch.ops.aten._scaled_dot_product_efficient_attention.default(
              primals_1,
              primals_2,
              primals_3,
              None,
              True,
              scale=0.17677669529663687,
          )[
              0
          ]
  
      def gn(*args):
          return torch.utils.checkpoint.checkpoint(
              fn, *args, use_reentrant=True
          )
  
      with torch.cuda.amp.autocast():
          x = torch.randn(
              4, 2, 16, 32, device="cuda", requires_grad=True
          )
          y = torch.randn(
              4, 2, 16, 32, device="cuda", requires_grad=True
          )
          z = torch.randn(
              4, 2, 16, 32, device="cuda", requires_grad=True
          )
          args = (x, y, z)
  
          torch.manual_seed(0)
          ref = gn(*args)
  
          opt_gn = torch.compile(gn)
          torch.manual_seed(0)
          res = opt_gn(*args)
          self.assertEqual(ref, res)
  
imports: import functools
  import unittest
  from importlib import import_module
  import torch
  import torch._dynamo.config
  import torch._dynamo.test_case
  import torch._functorch.config
  import torch.utils.checkpoint
  from functorch.compile import min_cut_rematerialization_partition
  from torch._dynamo.backends.common import aot_autograd
  from torch._dynamo.testing import CompileCounterWithBackend
  from torch._higher_order_ops.wrap import tag_activation_checkpoint
  from torch.testing._internal.common_utils import IS_WINDOWS, skipIfRocm
  from torch.testing._internal.inductor_utils import HAS_CUDA
  from torch.testing._internal.two_tensor import TwoTensor
  from torch.utils.checkpoint import _pt2_selective_checkpoint_context_fn_gen, checkpoint
label_desc: Use image node_id 22 for calling the ActivationCheckpointingViaTagsTests obj's underlying member method code with example usage: obj.test_autocast_flash_attention() and returns: torch, torch