code: def test_cuda_amp_autocast(self):
      class MyModule(torch.nn.Module):
          def forward(self, x):
              a_float32 = torch.rand((8, 8), device="cuda")
              b_float32 = torch.rand((8, 8), device="cuda")
  
              with torch.cuda.amp.autocast(dtype=torch.torch.float64):
                  c_float64 = torch.mm(a_float32, b_float32)
              return c_float64
  
      module = MyModule()
      real = module(torch.tensor([0.5]))
      real_device = real.device
      real_dtype = real.dtype
  
      graph, _ = torch._dynamo.export(module)(
          torch.tensor([[0.0, 0], [0, 0]])
      )
      exported = graph(torch.tensor([0.5]))
      self.assertEqual(exported.device, real_device)
      self.assertEqual(exported.dtype, real_dtype)
  
      self.assertEqual(exported.device.type, "cuda")
      self.assertEqual(exported.device.index, 0)
      self.assertEqual(exported.dtype, torch.float64)
  
imports: import unittest
  import torch
  import torch._dynamo.test_case
  import torch._dynamo.testing
  import torch.onnx.operators
  from torch._dynamo.testing import EagerAndRecordGraphs, normalize_gm, same
  from torch.nn import functional
  from torch.testing._internal.common_cuda import PLATFORM_SUPPORTS_FLASH_ATTENTION
  from torch.testing._internal.common_utils import TEST_WITH_ROCM
label_desc: Use image node_id 13 for calling the CtxManagerTests obj's underlying member method code with example usage: obj.test_cuda_amp_autocast() and returns: c_float64