code: def test_rand_like_dynamic(self, dtype, device):
      def fn(x):
          a = torch.rand_like(x) * x
          a = torch.rand_like(x) * a
          return a
  
      for seed in range(1, 10):
          shape = (seed, seed)
          x = torch.rand(shape, device=device, dtype=dtype)
          torch.cuda.manual_seed(seed)
          ref = fn(x)
  
          torch.cuda.manual_seed(seed)
          opt_fn = torch.compile(fn, backend="aot_eager", dynamic=True)
          res = opt_fn(x)
  
          self.assertEqual(ref, res)
  
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 2 for calling the TestFunctionalizationRngOps obj's underlying member method code with example usage: obj.test_rand_like_dynamic(dtype, device) and returns: a