class_docstr: Tests just-in-time cpp extensions.
  Don't confuse this with the PyTorch JIT (aka TorchScript).
code: def test_inline_jit_compile_extension_with_functions_as_dict(self):
      cpp_source = """
          torch::Tensor tanh_add(torch::Tensor x, torch::Tensor y) {
            return x.tanh() + y.tanh();
          }
          """
  
      module = torch.utils.cpp_extension.load_inline(
          name="inline_jit_extension_with_functions_dict",
          cpp_sources=cpp_source,
          functions={"tanh_add": "Tanh and then sum :D"},
          verbose=True,
      )
  
      self.assertEqual(
          module.tanh_add.__doc__.split("\n")[2], "Tanh and then sum :D"
      )
  
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 12 for calling the TestCppExtensionJIT obj's underlying member method code with example usage: obj.test_inline_jit_compile_extension_with_functions_as_dict() without return types