def unified_function_type(numba_types, require_precise=True):
    """Returns a unified Numba function type if possible.

    Parameters
    ----------
    numba_types : Sequence of numba Type instances.
    require_precise : bool
      If True, the returned Numba function type must be precise.

    Returns
    -------
    typ : {numba.core.types.Type, None}
      A unified Numba function type. Or ``None`` when the Numba types
      cannot be unified, e.g. when the ``numba_types`` contains at
      least two different Numba function type instances.

    If ``numba_types`` contains a Numba dispatcher type, the unified
    Numba function type will be an imprecise ``UndefinedFunctionType``
    instance, or None when ``require_precise=True`` is specified.

    Specifying ``require_precise=False`` enables unifying imprecise
    Numba dispatcher instances when used in tuples or if-then branches
    when the precise Numba function cannot be determined on the first
    occurrence that is not a call expression.
    """
    from numba.core.errors import NumbaExperimentalFeatureWarning

    if not (isinstance(numba_types, Sequence) and
            len(numba_types) > 0 and
            isinstance(numba_types[0],
                       (types.Dispatcher, types.FunctionType))):
        return

    warnings.warn("First-class function type feature is experimental",
                  category=NumbaExperimentalFeatureWarning)

    mnargs, mxargs = None, None
    dispatchers = set()
    function = None
    undefined_function = None

    for t in numba_types:
        if isinstance(t, types.Dispatcher):
            mnargs1, mxargs1 = get_nargs_range(t.dispatcher.py_func)
            if mnargs is None:
                mnargs, mxargs = mnargs1, mxargs1
            elif not (mnargs, mxargs) == (mnargs1, mxargs1):
                return
            dispatchers.add(t.dispatcher)
            t = t.dispatcher.get_function_type()
            if t is None:
                continue
        if isinstance(t, types.FunctionType):
            if mnargs is None:
                mnargs = mxargs = t.nargs
            elif not (mnargs == mxargs == t.nargs):
                return
            if isinstance(t, types.UndefinedFunctionType):
                if undefined_function is None:
                    undefined_function = t
                else:
                    # Refuse to unify using function type
                    return
                dispatchers.update(t.dispatchers)
            else:
                if function is None:
                    function = t
                else:
                    assert function == t
        else:
            return
    if require_precise and (function is None or undefined_function is not None):
        return
    if function is not None:
        if undefined_function is not None:
            assert function.nargs == undefined_function.nargs
            function = undefined_function
    elif undefined_function is not None:
        undefined_function.dispatchers.update(dispatchers)
        function = undefined_function
    else:
        function = types.UndefinedFunctionType(mnargs, dispatchers)

    return function
Returns a unified Numba function type if possible.

    Parameters
    ----------
    numba_types : Sequence of numba Type instances.
    require_precise : bool
      If True, the returned Numba function type must be precise.

    Returns
    -------
    typ : {numba.core.types.Type, None}
      A unified Numba function type. Or ``None`` when the Numba types
      cannot be unified, e.g. when the ``numba_types`` contains at
      least two different Numba function type instances.

    If ``numba_types`` contains a Numba dispatcher type, the unified
    Numba function type will be an imprecise ``UndefinedFunctionType``
    instance, or None when ``require_precise=True`` is specified.

    Specifying ``require_precise=False`` enables unifying imprecise
    Numba dispatcher instances when used in tuples or if-then branches
    when the precise Numba function cannot be determined on the first
    occurrence that is not a call expression.
    
numba/core/utils.py