code: def test_dont_change_dtype_folding(self):
      dtype = torch.float16 if self.device == "cuda" else torch.bfloat16
  
      mod = (
          torch.nn.Conv2d(3, 32, bias=None, kernel_size=3, stride=2)
          .eval()
          .to(self.device)
          .to(dtype)
      )
      x = torch.rand(3, 3, 32, 32).to(self.device).to(dtype)
  
      def foo(mod, x):
          return mod(x) * torch.full([1], 2.0, device=self.device)
  
      foo_c = torch.compile(foo)
  
      with torch.no_grad():
          out_eager = foo(mod, x)
          out_compiled = foo_c(mod, x)
          self.assertEqual(out_eager, out_compiled)
  
imports: import contextlib
  import functools
  import importlib
  import itertools
  import os
  import sys
  import unittest
  import weakref
  import torch
  from torch import nn
  from torch._inductor import config
  from torch._inductor.utils import override_lowering, run_and_get_code
  from torch.testing import FileCheck
  from torch.testing._internal.common_cuda import SM80OrLater
  from torch.testing._internal.common_utils import IS_CI, IS_WINDOWS, TEST_WITH_ASAN, TEST_WITH_ROCM, TestCase
  from inductor.test_torchinductor import check_model, check_model_cuda, copy_tests
  from torch.testing._internal.inductor_utils import HAS_CPU, HAS_CUDA
label_desc: Use image node_id 11 for calling the OptimizeForInferenceTemplate obj's underlying member method code with example usage: obj.test_dont_change_dtype_folding() and returns: unknown