# Buggy code
def generic(self, args, kws):
    """
    Type the overloaded function by compiling the appropriate
    implementation for the given args.
    """
    disp, new_args = self._get_impl(args, kws)
    if disp is None:
        return
    # Compile and type it for the given types
    disp_type = types.Dispatcher(disp)
    # Store the compiled overload for use in the lowering phase if there's
    # no inlining required (else functions are being compiled which will
    # never be used as they are inlined)
    if not self._inline.is_never_inline:
        # need to run the compiler front end up to type inference to compute
        # a signature
        from numba import compiler, typed_passes

        ir = compiler.run_frontend(disp_type.dispatcher.py_func)
        resolve = disp_type.dispatcher.get_call_template
        template, pysig, folded_args, kws = resolve(new_args, kws)

        typemap, return_type, calltypes = typed_passes.type_inference_stage(
            self.context, ir, folded_args, None
        )
        sig = Signature(return_type, folded_args, None)
        # this stores a load of info for the cost model function if supplied
        # it by default is None
        self._inline_overloads[sig.args] = {"folded_args": folded_args}
        # this stores the compiled overloads, if there's no compiled
        # overload available i.e. function is always inlined, the key still
        # needs to exist for type resolution
        self._compiled_overloads[sig.args] = None
        if not self._inline.is_always_inline:
            # this branch is here because a user has supplied a function to
            # determine whether to inline or not. As a result both compiled
            # function and inliner info needed, delaying the computation of
            # this leads to an internal state mess at present. TODO: Fix!
            sig = disp_type.get_call_type(self.context, new_args, kws)
            self._compiled_overloads[sig.args] = disp_type.get_overload(sig)
            # store the inliner information, it's used later in the cost
            # model function call
            iinfo = _inline_info(ir, typemap, calltypes, sig)
            self._inline_overloads[sig.args] = {
                "folded_args": folded_args,
                "iinfo": iinfo,
            }
    else:
        sig = disp_type.get_call_type(self.context, new_args, kws)
        self._compiled_overloads[sig.args] = disp_type.get_overload(sig)
    return sig


# 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 generic(self, args, kws):
    """
    Type the overloaded function by compiling the appropriate
    implementation for the given args.
    """
    disp, new_args = self._get_impl(args, kws)
    if disp is None:
        return
    # Compile and type it for the given types
    disp_type = types.Dispatcher(disp)
    # Store the compiled overload for use in the lowering phase if there's
    # no inlining required (else functions are being compiled which will
    # never be used as they are inlined)
    if not self._inline.is_never_inline:
        # need to run the compiler front end up to type inference to compute
        # a signature
        from numba import compiler, typed_passes

        ir = compiler.run_frontend(disp_type.dispatcher.py_func)
        resolve = disp_type.dispatcher.get_call_template
        template, pysig, folded_args, kws = resolve(new_args, kws)

        typemap, return_type, calltypes = typed_passes.type_inference_stage(
            self.context, ir, folded_args, None
        )
        sig = Signature(return_type, folded_args, None)
        # this stores a load of info for the cost model function if supplied
        # it by default is None
        self._inline_overloads[sig.args] = {"folded_args": folded_args}
        # this stores the compiled overloads, if there's no compiled
        # overload available i.e. function is always inlined, the key still
        # needs to exist for type resolution

        # NOTE: If lowering is failing on a `_EmptyImplementationEntry`,
        #       the inliner has failed to inline this entry corretly.
        impl_init = _EmptyImplementationEntry("always inlined")
        self._compiled_overloads[sig.args] = impl_init
        if not self._inline.is_always_inline:
            # this branch is here because a user has supplied a function to
            # determine whether to inline or not. As a result both compiled
            # function and inliner info needed, delaying the computation of
            # this leads to an internal state mess at present. TODO: Fix!
            sig = disp_type.get_call_type(self.context, new_args, kws)
            self._compiled_overloads[sig.args] = disp_type.get_overload(sig)
            # store the inliner information, it's used later in the cost
            # model function call
            iinfo = _inline_info(ir, typemap, calltypes, sig)
            self._inline_overloads[sig.args] = {
                "folded_args": folded_args,
                "iinfo": iinfo,
            }
    else:
        sig = disp_type.get_call_type(self.context, new_args, kws)
        self._compiled_overloads[sig.args] = disp_type.get_overload(sig)
    return sig