# Buggy code
def _build_impl(self, cache_key, args, kws):
    """Build and cache the implementation.

    Given the positional (`args`) and keyword arguments (`kws`), obtains
    the `overload` implementation and wrap it in a Dispatcher object.
    The expected argument types are returned for use by type-inference.
    The expected argument types are only different from the given argument
    types if there is an imprecise type in the given argument types.

    Parameters
    ----------
    cache_key : hashable
        The key used for caching the implementation.
    args : Tuple[Type]
        Types of positional argument.
    kws : Dict[Type]
        Types of keyword argument.

    Returns
    -------
    disp, args :
        On success, returns `(Dispatcher, Tuple[Type])`.
        On failure, returns `(None, None)`.

    """
    from numba import jit

    # Get the overload implementation for the given types
    ovf_result = self._overload_func(*args, **kws)
    if ovf_result is None:
        # No implementation => fail typing
        self._impl_cache[cache_key] = None, None
        return None, None
    elif isinstance(ovf_result, tuple):
        # The implementation returned a signature that the type-inferencer
        # should be using.
        sig, pyfunc = ovf_result
        args = sig.args
        cache_key = None  # don't cache
    else:
        # Regular case
        pyfunc = ovf_result

    # Check type of pyfunc
    if not isinstance(pyfunc, FunctionType):
        msg = (
            "Implementator function returned by `@overload` "
            "has an unexpected type.  Got {}"
        )
        raise AssertionError(msg.format(pyfunc))

    # check that the typing and impl sigs match up
    if self._strict:
        self._validate_sigs(self._overload_func, pyfunc)
    # Make dispatcher
    jitdecor = jit(nopython=True, **self._jit_options)
    disp = jitdecor(pyfunc)
    if cache_key is not None:
        self._impl_cache[cache_key] = disp, args
    return disp, args


# Traceback (IndexError)
Traceback (most recent call last):
File "/home/ibutygin/miniconda3/envs/NUMBA_CUSTOM/lib/python3.7/site-packages/llvmlite/ir/instructions.py", line 619, in __init__
typ = typ.elements[i]
IndexError: tuple index out of range

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
File "/home/ibutygin/src/numba/numba/numba/errors.py", line 717, in new_error_context
yield
File "/home/ibutygin/src/numba/numba/numba/lowering.py", line 260, in lower_block
self.lower_inst(inst)
File "/home/ibutygin/src/numba/numba/numba/lowering.py", line 303, in lower_inst
val = self.lower_assign(ty, inst)
File "/home/ibutygin/src/numba/numba/numba/lowering.py", line 467, in lower_assign
return self.lower_expr(ty, value)
File "/home/ibutygin/src/numba/numba/numba/lowering.py", line 932, in lower_expr
res = self.lower_call(resty, expr)
File "/home/ibutygin/src/numba/numba/numba/lowering.py", line 724, in lower_call
res = self._lower_call_normal(fnty, expr, signature)
File "/home/ibutygin/src/numba/numba/numba/lowering.py", line 903, in _lower_call_normal
res = impl(self.builder, argvals, self.loc)
File "/home/ibutygin/src/numba/numba/numba/targets/base.py", line 1132, in __call__
res = self._imp(self._context, builder, self._sig, args, loc=loc)
File "/home/ibutygin/src/numba/numba/numba/targets/base.py", line 1157, in wrapper
return fn(*args, **kwargs)
File "/home/ibutygin/src/numba/numba/numba/typing/templates.py", line 778, in method_impl
return call(builder, args)
File "/home/ibutygin/src/numba/numba/numba/targets/base.py", line 1132, in __call__
res = self._imp(self._context, builder, self._sig, args, loc=loc)
File "/home/ibutygin/src/numba/numba/numba/targets/base.py", line 1157, in wrapper
return fn(*args, **kwargs)
File "/home/ibutygin/src/numba/numba/numba/targets/imputils.py", line 193, in imp
builder, func, fndesc.restype, fndesc.argtypes, args)
File "/home/ibutygin/src/numba/numba/numba/targets/callconv.py", line 477, in call_function
args = list(arginfo.as_arguments(builder, args))
File "/home/ibutygin/src/numba/numba/numba/datamodel/packer.py", line 100, in as_arguments
for dm, val in zip(self._dm_args, values)
File "/home/ibutygin/src/numba/numba/numba/datamodel/packer.py", line 100, in <listcomp>
for dm, val in zip(self._dm_args, values)
File "/home/ibutygin/src/numba/numba/numba/datamodel/models.py", line 447, in as_argument
v = builder.extract_value(value, [i])
File "/home/ibutygin/miniconda3/envs/NUMBA_CUSTOM/lib/python3.7/site-packages/llvmlite/ir/builder.py", line 927, in extract_value
instr = instructions.ExtractValue(self.block, agg, idx, name=name)
File "/home/ibutygin/miniconda3/envs/NUMBA_CUSTOM/lib/python3.7/site-packages/llvmlite/ir/instructions.py", line 622, in __init__
% (list(indices), agg.type))
TypeError: Can't index at [0] in {}

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
File "test.py", line 73, in <module>
print(bar(Dummy()))
File "/home/ibutygin/src/numba/numba/numba/dispatcher.py", line 420, in _compile_for_args
raise e
File "/home/ibutygin/src/numba/numba/numba/dispatcher.py", line 353, in _compile_for_args
return self.compile(tuple(argtypes))
File "/home/ibutygin/src/numba/numba/numba/compiler_lock.py", line 32, in _acquire_compile_lock
return func(*args, **kwargs)
File "/home/ibutygin/src/numba/numba/numba/dispatcher.py", line 768, in compile
cres = self._compiler.compile(args, return_type)
File "/home/ibutygin/src/numba/numba/numba/dispatcher.py", line 77, in compile
status, retval = self._compile_cached(args, return_type)
File "/home/ibutygin/src/numba/numba/numba/dispatcher.py", line 91, in _compile_cached
retval = self._compile_core(args, return_type)
File "/home/ibutygin/src/numba/numba/numba/dispatcher.py", line 109, in _compile_core
pipeline_class=self.pipeline_class)
File "/home/ibutygin/src/numba/numba/numba/compiler.py", line 543, in compile_extra
return pipeline.compile_extra(func)
File "/home/ibutygin/src/numba/numba/numba/compiler.py", line 328, in compile_extra
return self._compile_bytecode()
File "/home/ibutygin/src/numba/numba/numba/compiler.py", line 389, in _compile_bytecode
return self._compile_core()
File "/home/ibutygin/src/numba/numba/numba/compiler.py", line 368, in _compile_core
raise e
File "/home/ibutygin/src/numba/numba/numba/compiler.py", line 359, in _compile_core
pm.run(self.state)
File "/home/ibutygin/src/numba/numba/numba/compiler_machinery.py", line 331, in run
raise patched_exception
File "/home/ibutygin/src/numba/numba/numba/compiler_machinery.py", line 322, in run
self._runPass(idx, pass_inst, state)
File "/home/ibutygin/src/numba/numba/numba/compiler_lock.py", line 32, in _acquire_compile_lock
return func(*args, **kwargs)
File "/home/ibutygin/src/numba/numba/numba/compiler_machinery.py", line 281, in _runPass
mutated |= check(pss.run_pass, internal_state)
File "/home/ibutygin/src/numba/numba/numba/compiler_machinery.py", line 268, in check
mangled = func(compiler_state)
File "/home/ibutygin/src/numba/numba/numba/typed_passes.py", line 391, in run_pass
NativeLowering().run_pass(state)
File "/home/ibutygin/src/numba/numba/numba/typed_passes.py", line 333, in run_pass
lower.lower()
File "/home/ibutygin/src/numba/numba/numba/lowering.py", line 179, in lower
self.lower_normal_function(self.fndesc)
File "/home/ibutygin/src/numba/numba/numba/lowering.py", line 220, in lower_normal_function
entry_block_tail = self.lower_function_body()
File "/home/ibutygin/src/numba/numba/numba/lowering.py", line 245, in lower_function_body
self.lower_block(block)
File "/home/ibutygin/src/numba/numba/numba/lowering.py", line 260, in lower_block
self.lower_inst(inst)
File "/home/ibutygin/miniconda3/envs/NUMBA_CUSTOM/lib/python3.7/contextlib.py", line 130, in __exit__
self.gen.throw(type, value, traceback)
File "/home/ibutygin/src/numba/numba/numba/errors.py", line 725, in new_error_context
six.reraise(type(newerr), newerr, tb)
File "/home/ibutygin/src/numba/numba/numba/six.py", line 669, in reraise
raise value
numba.errors.LoweringError: Failed in nopython mode pipeline (step: nopython mode backend)
Can't index at [0] in {}

File "test.py", line 71:
def bar(obj):
return obj.inline_method(123,456)
^

[1] During: lowering "$10call_method.4 = call $4load_method.1($const6.2, $const8.3, func=$4load_method.1, args=[Var($const6.2, test.py:71), Var($const8.3, test.py:71)], kws=(), vararg=None)" at test.py (71)

# Fixed code
def _build_impl(self, cache_key, args, kws):
    """Build and cache the implementation.

    Given the positional (`args`) and keyword arguments (`kws`), obtains
    the `overload` implementation and wrap it in a Dispatcher object.
    The expected argument types are returned for use by type-inference.
    The expected argument types are only different from the given argument
    types if there is an imprecise type in the given argument types.

    Parameters
    ----------
    cache_key : hashable
        The key used for caching the implementation.
    args : Tuple[Type]
        Types of positional argument.
    kws : Dict[Type]
        Types of keyword argument.

    Returns
    -------
    disp, args :
        On success, returns `(Dispatcher, Tuple[Type])`.
        On failure, returns `(None, None)`.

    """
    from numba import jit

    def normalize_args(pyfunc, args, kws):
        pysig = utils.pysignature(pyfunc)
        ba = pysig.bind(*args, **kws)
        starargs = None
        for i, (k, parm) in enumerate(pysig.parameters.items()):
            if parm.kind == parm.VAR_POSITIONAL and k in ba.arguments:
                starargs = ba.arguments[k]
                del ba.arguments[k]

        new_args = ba.args
        if starargs is not None:
            if (
                len(starargs)
                and isinstance(starargs[0], types.Tuple)
                and len(starargs[0]) == 0
            ):
                # empty tuple means no starargs
                pass
            else:
                new_args += starargs

        new_kws = ba.kwargs
        return new_args, new_kws

    # args, kws = normalize_args(self._overload_func, args, kws)

    # Get the overload implementation for the given types
    ovf_result = self._overload_func(*args, **kws)
    if ovf_result is None:
        # No implementation => fail typing
        self._impl_cache[cache_key] = None, None
        return None, None
    elif isinstance(ovf_result, tuple):
        # The implementation returned a signature that the type-inferencer
        # should be using.
        sig, pyfunc = ovf_result
        args = sig.args
        cache_key = None  # don't cache
    else:
        # Regular case
        pyfunc = ovf_result

    # Check type of pyfunc
    if not isinstance(pyfunc, FunctionType):
        msg = (
            "Implementator function returned by `@overload` "
            "has an unexpected type.  Got {}"
        )
        raise AssertionError(msg.format(pyfunc))

    # check that the typing and impl sigs match up
    if self._strict:
        self._validate_sigs(self._overload_func, pyfunc)
    # Make dispatcher
    jitdecor = jit(nopython=True, **self._jit_options)
    disp = jitdecor(pyfunc)
    if cache_key is not None:
        self._impl_cache[cache_key] = disp, args
    return disp, args