code: def test_autocast_cpu_graph_break_inner_fn(self):
      class MyModule(torch.nn.Module):
          @staticmethod
          def mm_breaks(x, y):
              torch._dynamo.graph_break()
              return torch.mm(x, y)
  
          def forward(self, x):
              a_float32 = torch.rand((8, 8), device="cpu")
              b_float32 = torch.rand((8, 8), device="cpu")
  
              with torch.autocast(
                  device_type="cpu", dtype=torch.bfloat16
              ):
                  torch._dynamo.graph_break()
                  with torch.autocast(
                      device_type="cpu",
                      dtype=torch.bfloat16,
                      enabled=False,
                  ):
                      torch._dynamo.graph_break()
                      g_float32 = torch.mm(a_float32, b_float32)
                      with torch.autocast(
                          device_type="cpu", dtype=torch.bfloat16
                      ):
                          # Check that nested with non-inlineable function with graph break
                          torch._dynamo.graph_break()
                          f_float16_1 = self.mm_breaks(
                              a_float32, b_float32
                          )
                  # We remember to exit the inner autocast correctly to outer
                  # even after graph breaks
                  f_float16 = self.mm_breaks(a_float32, b_float32)
                  assert f_float16.dtype == f_float16_1.dtype
              return f_float16, g_float32
  
      module = MyModule()
      real_16, real_32 = module(torch.tensor([0.5]))
      real_device_16 = real_16.device
      real_dtype_16 = real_16.dtype
      real_device_32 = real_32.device
      real_dtype_32 = real_32.dtype
  
      graph = torch._dynamo.optimize("eager")(module)
      out_16, out_32 = graph(torch.tensor([0.5]))
      self.assertEqual(out_16.device, real_device_16)
      self.assertEqual(out_16.dtype, real_dtype_16)
      self.assertEqual(out_32.device, real_device_32)
      self.assertEqual(out_32.dtype, real_dtype_32)
  
      self.assertEqual(out_16.device.type, "cpu")
      self.assertEqual(out_16.dtype, torch.bfloat16)
      self.assertEqual(out_32.device.type, "cpu")
      self.assertEqual(out_32.dtype, torch.float32)
  
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 19 for calling the CtxManagerTests obj's underlying member method code with example usage: obj.test_autocast_cpu_graph_break_inner_fn() and returns: torch, f_float16,  g_float32