code: def test_dropout_decomp(self, dtype, device):
      def fn(x):
          return torch.nn.functional.dropout(x, 0.6) * x
  
      x = torch.rand(10, device=device, dtype=dtype)
  
      # Ensure the decomp is happening
      aot_fn = aot_function(
          fn, functools.partial(count_philox_rand, freq=1)
      )
      # We cant check accuracy here because rand_like generated different rand numbers than dropout
      aot_fn(x)
  
imports: import sys
  import unittest
  import torch
  from torch.testing._internal.common_utils import TestCase, run_tests
  from torch.testing._internal.common_device_type import instantiate_device_type_tests, dtypes
  from functorch.compile import aot_function, nop, min_cut_rematerialization_partition
  from unittest.mock import patch
  import functools
  import torch.utils.checkpoint
  from torch.testing._internal.common_utils import IS_CI, IS_WINDOWS
label_desc: Use image node_id 10 for calling the TestFunctionalizationRngOps obj's underlying member method code with example usage: obj.test_dropout_decomp(dtype, device) and returns: unknown