class_docstr: Tests just-in-time cpp extensions.
  Don't confuse this with the PyTorch JIT (aka TorchScript).
code: def test_custom_functorch_error(self):
      # Test that a custom C++ Function raises an error under functorch transforms
      identity_m = torch.utils.cpp_extension.load(
          name="identity",
          sources=["cpp_extensions/identity.cpp"],
      )
  
      t = torch.randn(3, requires_grad=True)
  
      msg = r"cannot use C\+\+ torch::autograd::Function with functorch"
      with self.assertRaisesRegex(RuntimeError, msg):
          torch.func.vmap(identity_m.identity)(t)
  
      with self.assertRaisesRegex(RuntimeError, msg):
          torch.func.grad(identity_m.identity)(t)
  
imports: import os
  import shutil
  import sys
  import unittest
  import warnings
  import re
  import tempfile
  import subprocess
  import glob
  import torch.testing._internal.common_utils
  from torch.testing._internal.common_cuda import TEST_CUDNN, TEST_CUDA
  import torch
  import torch.backends.cudnn
  import torch.utils.cpp_extension
  from torch.utils.cpp_extension import CUDA_HOME, ROCM_HOME
  from torch.testing._internal.common_utils import gradcheck
  import torch.multiprocessing
  from torch.utils.cpp_extension import _TORCH_PATH, remove_extension_h_precompiler_headers, get_cxx_compiler, check_compiler_is_gcc
label_desc: Use image node_id 30 for calling the TestCppExtensionJIT obj's underlying member method code with example usage: obj.test_custom_functorch_error() without return types