code: def test_tags_recomputed_rand(self):
      def gn(x, y):
          return torch.sigmoid(torch.rand_like(x) * y) * x
  
      def fn(x, y):
          x = torch.sin(x)
          x = torch.utils.checkpoint.checkpoint(
              gn, x, y, use_reentrant=True
          )
          x = torch.sin(x)
          z = torch.utils.checkpoint.checkpoint(
              gn, x, y, use_reentrant=True
          )
          return z
  
      x = torch.randn(4, 4, device="cuda", requires_grad=True)
      y = torch.randn(4, 4, device="cuda", requires_grad=True)
  
      # fw_compiler = functools.partial(count_ops, freq=2, op=torch.ops.aten.mm.default)
      # bw_compiler = functools.partial(
      #     count_ops, freq=6, op=torch.ops.aten.mm.default
      # )  # mm recomputed in the bwd
      # backend = aot_autograd(fw_compiler=fw_compiler, bw_compiler=bw_compiler)
      backend = "inductor"
      self._validate(fn, backend, x, y)
  
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 9 for calling the ActivationCheckpointingViaTagsTests obj's underlying member method code with example usage: obj.test_tags_recomputed_rand() and returns: unknown, z