Optional numba support

[jorenham]

Add contrib.numba, and use a noop @jit if not available on relevant functions.

Additionally, the scipy.integrate.quad integrand can be sped up with cfunc, see https://numba.readthedocs.io/en/stable/user/cfunc.html#example

In places where scipy.special functions are used, some trickery is needed.
For example, this snippet is used to make scipy.special.erfi work within numba jitted functions for np.float64 input:

# lmo/contrib/numba.py
def _overload_scipy_special_erfi():
    _erfi_f8 = ctypes.CFUNCTYPE(ctypes.c_double, ctypes.c_double)(
        numba.extending.get_cython_function_address(
            'scipy.special.cython_special',
            '__pyx_fuse_1erfi',
        ),
    )
    
    @numba.extending.overload(scipy.special.erfi)
    def numba_erfi(*args):
        match args:
            case (numba.types.Float(),):
                def _numba_erfi(*args):
                    return _erfi_f8(*args)
                return _numba_erfi
            case _:
                return None

def overload_scipy_special():
    _overload_scipy_special_erfi()

# lmo/pyproject.toml
[tool.poetry.plugins.numba_extensions]
init = "lmo.contrib.numba:overload_scipy_special"