code: def test_autocast_cpu_graph_break(self):
      class MyModule(torch.nn.Module):
          def forward(self, x):
              a_float32 = torch.rand((8, 8), device="cpu")
              b_float32 = torch.rand((8, 8), device="cpu")
              torch._dynamo.graph_break()
              d_float32 = torch.rand((8, 8), device="cpu")
  
              with torch.autocast(
                  device_type="cpu", dtype=torch.bfloat16
              ):
                  e_float16 = torch.mm(a_float32, b_float32)
                  torch._dynamo.graph_break()
                  f_float16 = torch.mm(d_float32, e_float16)
              return f_float16
  
      module = MyModule()
      real = module(torch.tensor([0.5]))
      real_device = real.device
      real_dtype = real.dtype
  
      opt = torch._dynamo.optimize("eager")(module)
      res = opt(torch.tensor([0.5]))
      self.assertEqual(res.device, real_device)
      self.assertEqual(res.dtype, real_dtype)
  
      self.assertEqual(res.device.type, "cpu")
      self.assertEqual(res.dtype, torch.bfloat16)
  
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 17 for calling the CtxManagerTests obj's underlying member method code with example usage: obj.test_autocast_cpu_graph_break() and returns: f_float16