# Buggy code
def jit(signature_or_function=None, locals={}, target="cpu", cache=False, **options):
    """
    This decorator is used to compile a Python function into native code.

    Args
    -----
    signature:
        The (optional) signature or list of signatures to be compiled.
        If not passed, required signatures will be compiled when the
        decorated function is called, depending on the argument values.
        As a convenience, you can directly pass the function to be compiled
        instead.

    locals: dict
        Mapping of local variable names to Numba types. Used to override the
        types deduced by Numba's type inference engine.

    target: str
        Specifies the target platform to compile for. Valid targets are cpu,
        gpu, npyufunc, and cuda. Defaults to cpu.

    options:
        For a cpu target, valid options are:
            nopython: bool
                Set to True to disable the use of PyObjects and Python API
                calls. The default behavior is to allow the use of PyObjects
                and Python API. Default value is False.

            forceobj: bool
                Set to True to force the use of PyObjects for every value.
                Default value is False.

            looplift: bool
                Set to True to enable jitting loops in nopython mode while
                leaving surrounding code in object mode. This allows functions
                to allocate NumPy arrays and use Python objects, while the
                tight loops in the function can still be compiled in nopython
                mode. Any arrays that the tight loop uses should be created
                before the loop is entered. Default value is True.

            error_model: str
                The error-model affects divide-by-zero behavior.
                Valid values are 'python' and 'numpy'. The 'python' model
                raises exception.  The 'numpy' model sets the result to
                *+/-inf* or *nan*.

    Returns
    --------
    A callable usable as a compiled function.  Actual compiling will be
    done lazily if no explicit signatures are passed.

    Examples
    --------
    The function can be used in the following ways:

    1) jit(signatures, target='cpu', **targetoptions) -> jit(function)

        Equivalent to:

            d = dispatcher(function, targetoptions)
            for signature in signatures:
                d.compile(signature)

        Create a dispatcher object for a python function.  Then, compile
        the function with the given signature(s).

        Example:

            @jit("int32(int32, int32)")
            def foo(x, y):
                return x + y

            @jit(["int32(int32, int32)", "float32(float32, float32)"])
            def bar(x, y):
                return x + y

    2) jit(function, target='cpu', **targetoptions) -> dispatcher

        Create a dispatcher function object that specializes at call site.

        Examples:

            @jit
            def foo(x, y):
                return x + y

            @jit(target='cpu', nopython=True)
            def bar(x, y):
                return x + y

    """
    if "argtypes" in options:
        raise DeprecationError(_msg_deprecated_signature_arg.format("argtypes"))
    if "restype" in options:
        raise DeprecationError(_msg_deprecated_signature_arg.format("restype"))

    # Handle signature
    if signature_or_function is None:
        # No signature, no function
        pyfunc = None
        sigs = None
    elif isinstance(signature_or_function, list):
        # A list of signatures is passed
        pyfunc = None
        sigs = signature_or_function
    elif sigutils.is_signature(signature_or_function):
        # A single signature is passed
        pyfunc = None
        sigs = [signature_or_function]
    else:
        # A function is passed
        pyfunc = signature_or_function
        sigs = None

    wrapper = _jit(
        sigs, locals=locals, target=target, cache=cache, targetoptions=options
    )
    if pyfunc is not None:
        return wrapper(pyfunc)
    else:
        return wrapper


# Traceback (TypeError)
---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-15-3b23f7331ceb> in <module>()
----> 1 @jit(arg_types=[numba.double], ret_type=numba.double)
2 def is_REALLY_five(some_value):
3     for i in range(5):
4         if some_value == 5.0:
5             return 1.0

/Library/Frameworks/EPD64.framework/Versions/7.3/lib/python2.7/site-packages/numba/decorators.pyc in _jit(func)
77                   "garbage collected!" % (func,))
78         t = Translate(func, *args, **kws)
---> 79         t.translate()
80         __tr_map__[func] = t
81         return t.get_ctypes_func(llvm)

/Library/Frameworks/EPD64.framework/Versions/7.3/lib/python2.7/site-packages/numba/translate.pyc in translate(self)
926                     # Ensure we are playing with locals that might
927                     # actually precede the next block.
--> 928                     self.check_locals(i)
929
930                 self.crnt_block = i

/Library/Frameworks/EPD64.framework/Versions/7.3/lib/python2.7/site-packages/numba/translate.pyc in check_locals(self, i)
1135                 else:
1136                     assert next_locals is not None, "Internal compiler error!"
-> 1137             self._locals = next_locals[:]
1138
1139     def get_ctypes_func(self, llvm=True):

TypeError: 'NoneType' object has no attribute '__getitem__'

# Fixed code
def jit(signature_or_function=None, locals={}, target="cpu", cache=False, **options):
    """
    This decorator is used to compile a Python function into native code.

    Args
    -----
    signature:
        The (optional) signature or list of signatures to be compiled.
        If not passed, required signatures will be compiled when the
        decorated function is called, depending on the argument values.
        As a convenience, you can directly pass the function to be compiled
        instead.

    locals: dict
        Mapping of local variable names to Numba types. Used to override the
        types deduced by Numba's type inference engine.

    target: str
        Specifies the target platform to compile for. Valid targets are cpu,
        gpu, npyufunc, and cuda. Defaults to cpu.

    options:
        For a cpu target, valid options are:
            nopython: bool
                Set to True to disable the use of PyObjects and Python API
                calls. The default behavior is to allow the use of PyObjects
                and Python API. Default value is False.

            forceobj: bool
                Set to True to force the use of PyObjects for every value.
                Default value is False.

            looplift: bool
                Set to True to enable jitting loops in nopython mode while
                leaving surrounding code in object mode. This allows functions
                to allocate NumPy arrays and use Python objects, while the
                tight loops in the function can still be compiled in nopython
                mode. Any arrays that the tight loop uses should be created
                before the loop is entered. Default value is True.

            error_model: str
                The error-model affects divide-by-zero behavior.
                Valid values are 'python' and 'numpy'. The 'python' model
                raises exception.  The 'numpy' model sets the result to
                *+/-inf* or *nan*.

    Returns
    --------
    A callable usable as a compiled function.  Actual compiling will be
    done lazily if no explicit signatures are passed.

    Examples
    --------
    The function can be used in the following ways:

    1) jit(signatures, target='cpu', **targetoptions) -> jit(function)

        Equivalent to:

            d = dispatcher(function, targetoptions)
            for signature in signatures:
                d.compile(signature)

        Create a dispatcher object for a python function.  Then, compile
        the function with the given signature(s).

        Example:

            @jit("int32(int32, int32)")
            def foo(x, y):
                return x + y

            @jit(["int32(int32, int32)", "float32(float32, float32)"])
            def bar(x, y):
                return x + y

    2) jit(function, target='cpu', **targetoptions) -> dispatcher

        Create a dispatcher function object that specializes at call site.

        Examples:

            @jit
            def foo(x, y):
                return x + y

            @jit(target='cpu', nopython=True)
            def bar(x, y):
                return x + y

    """
    if "argtypes" in options:
        raise DeprecationError(_msg_deprecated_signature_arg.format("argtypes"))
    if "restype" in options:
        raise DeprecationError(_msg_deprecated_signature_arg.format("restype"))

    if options.get("parallel"):
        uns1 = sys.platform.startswith("win32") and sys.version_info[:2] == (2, 7)
        uns2 = sys.maxsize <= 2**32
        if uns1 or uns2:
            msg = (
                "The 'parallel' target is not currently supported on "
                "Windows operating systems when using Python 2.7, or "
                "on 32 bit hardware."
            )
            raise RuntimeError(msg)
        if cache:
            msg = (
                "Caching is not available when the 'parallel' target is in "
                "use. Caching is now being disabled to allow execution to "
                "continue."
            )
            warnings.warn(msg, RuntimeWarning)
            cache = False

    # Handle signature
    if signature_or_function is None:
        # No signature, no function
        pyfunc = None
        sigs = None
    elif isinstance(signature_or_function, list):
        # A list of signatures is passed
        pyfunc = None
        sigs = signature_or_function
    elif sigutils.is_signature(signature_or_function):
        # A single signature is passed
        pyfunc = None
        sigs = [signature_or_function]
    else:
        # A function is passed
        pyfunc = signature_or_function
        sigs = None

    wrapper = _jit(
        sigs, locals=locals, target=target, cache=cache, targetoptions=options
    )
    if pyfunc is not None:
        return wrapper(pyfunc)
    else:
        return wrapper