le your extension
    with optimizations, pass ``extra_cflags=['-O3']``. You can also use
    ``extra_cflags`` to pass further include directories.

    CUDA support with mixed compilation is provided. Simply pass CUDA source
    files (``.cu`` or ``.cuh``) along with other sources. Such files will be
    detected and compiled with nvcc rather than the C++ compiler. This includes
    passing the CUDA lib64 directory as a library directory, and linking
    ``cudart``. You can pass additional flags to nvcc via
    ``extra_cuda_cflags``, just like with ``extra_cflags`` for C++. Various
    heuristics for finding the CUDA install directory are used, which usually
    work fine. If not, setting the ``CUDA_HOME`` environment variable is the
    safest option.

    Args:
        name: The name of the extension to build. This MUST be the same as the
            name of the pybind11 module!
        sources: A list of relative or absolute paths to C++ source files.
        extra_cflags: optional list of compiler flags to forward to the build.
        extra_cuda_cflags: optional list of compiler flags to forward to nvcc
            when building CUDA sources.
        extra_ldflags: optional list of linker flags to forward to the build.
        extra_include_paths: optional list of include directories to forward
            to the build.
        build_directory: optional path to use as build workspace.
        verbose: If ``True``, turns on verbose logging of load steps.
        with_cuda: Determines whether CUDA headers and libraries are added to
            the build. If set to ``None`` (default), this value is
            automatically determined based on the existence of ``.cu`` or
            ``.cuh`` in ``sources``. Set it to `True`` to force CUDA headers
            and libraries to be included.
        is_python_module: If ``True`` (default), imports the produced shared
            library as a Python module. If ``False``, behavior depends on
            ``is_standalone``.
        is_standalone: If ``False`` (default) loads the constructed extension
            into the process as a plain dynamic library. If ``True``, build a
            standalone executable.

    Returns:
        If ``is_python_module`` is ``True``:
            Returns the loaded PyTorch extension as a Python module.

        If ``is_python_module`` is ``False`` and ``is_standalone`` is ``False``:
            Returns nothing. (The shared library is loaded into the process as
            a side effect.)

        If ``is_standalone`` is ``True``.
            Return the path to the executable. (On Windows, TORCH_LIB_PATH is
            added to the PATH environment variable as a side effect.)

    Example:
        >>> # xdoctest: +SKIP
        >>> from torch.utils.cpp_extension import load
        >>> module = load(
        ...     name='extension',
        ...     sources=['extension.cpp', 'extension_kernel.cu'],
        ...     extra_cflags=['-O2'],
        ...     verbose=True)
    ©