# Buggy code
def np_interp_impl_inner(x, xp, fp, dtype):
    # NOTE: Do not refactor... see note in np_interp function impl below
    # this is a facsimile of arr_interp prior to 1.16:
    # https://github.com/numpy/numpy/blob/maintenance/1.15.x/numpy/core/src/multiarray/compiled_base.c    # noqa: E501
    # Permanent reference:
    # https://github.com/numpy/numpy/blob/3430d78c01a3b9a19adad75f1acb5ae18286da73/numpy/core/src/multiarray/compiled_base.c#L532    # noqa: E501
    dz = np.asarray(x)
    dx = np.asarray(xp)
    dy = np.asarray(fp)

    if len(dx) == 0:
        raise ValueError("array of sample points is empty")

    if len(dx) != len(dy):
        raise ValueError("fp and xp are not of the same size.")

    if dx.size == 1:
        return np.full(dz.shape, fill_value=dy[0], dtype=dtype)

    dres = np.empty(dz.shape, dtype=dtype)

    lenx = dz.size
    lenxp = len(dx)
    lval = dy[0]
    rval = dy[lenxp - 1]

    if lenxp == 1:
        xp_val = dx[0]
        fp_val = dy[0]

        for i in range(lenx):
            x_val = dz.flat[i]
            if x_val < xp_val:
                dres.flat[i] = lval
            elif x_val > xp_val:
                dres.flat[i] = rval
            else:
                dres.flat[i] = fp_val

    else:
        j = 0

        # only pre-calculate slopes if there are relatively few of them.
        if lenxp <= lenx:
            slopes = (dy[1:] - dy[:-1]) / (dx[1:] - dx[:-1])
        else:
            slopes = np.empty(0, dtype=dtype)

        for i in range(lenx):
            x_val = dz.flat[i]

            if np.isnan(x_val):
                dres.flat[i] = x_val
                continue

            j = binary_search_with_guess(x_val, dx, lenxp, j)

            if j == -1:
                dres.flat[i] = lval
            elif j == lenxp:
                dres.flat[i] = rval
            elif j == lenxp - 1:
                dres.flat[i] = dy[j]
            else:
                if slopes.size:
                    slope = slopes[j]
                else:
                    slope = (dy[j + 1] - dy[j]) / (dx[j + 1] - dx[j])

                dres.flat[i] = slope * (x_val - dx[j]) + dy[j]

                # NOTE: this is in master but not in any released
                # version of 1.16.x yet...
                #
                # If we get nan in one direction, try the other
                # if np.isnan(dres.flat[i]):
                #     dres.flat[i] = slope * (x_val - dx[j + 1]) + dy[j + 1]
                #
                #     if np.isnan(dres.flat[i]) and dy[j] == dy[j + 1]:
                #         dres.flat[i] = dy[j]

    return dres


# 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 np_interp_impl_inner(x, xp, fp, dtype):
    # NOTE: Do not refactor... see note in np_interp function impl below
    # this is a facsimile of arr_interp prior to 1.16:
    # https://github.com/numpy/numpy/blob/maintenance/1.15.x/numpy/core/src/multiarray/compiled_base.c    # noqa: E501
    # Permanent reference:
    # https://github.com/numpy/numpy/blob/3430d78c01a3b9a19adad75f1acb5ae18286da73/numpy/core/src/multiarray/compiled_base.c#L532    # noqa: E501
    dz = np.asarray(x, dtype=np.float64)
    dx = np.asarray(xp, dtype=np.float64)
    dy = np.asarray(fp, dtype=np.float64)

    if len(dx) == 0:
        raise ValueError("array of sample points is empty")

    if len(dx) != len(dy):
        raise ValueError("fp and xp are not of the same size.")

    if dx.size == 1:
        return np.full(dz.shape, fill_value=dy[0], dtype=dtype)

    dres = np.empty(dz.shape, dtype=dtype)

    lenx = dz.size
    lenxp = len(dx)
    lval = dy[0]
    rval = dy[lenxp - 1]

    if lenxp == 1:
        xp_val = dx[0]
        fp_val = dy[0]

        for i in range(lenx):
            x_val = dz.flat[i]
            if x_val < xp_val:
                dres.flat[i] = lval
            elif x_val > xp_val:
                dres.flat[i] = rval
            else:
                dres.flat[i] = fp_val

    else:
        j = 0

        # only pre-calculate slopes if there are relatively few of them.
        if lenxp <= lenx:
            slopes = (dy[1:] - dy[:-1]) / (dx[1:] - dx[:-1])
        else:
            slopes = np.empty(0, dtype=dtype)

        for i in range(lenx):
            x_val = dz.flat[i]

            if np.isnan(x_val):
                dres.flat[i] = x_val
                continue

            j = binary_search_with_guess(x_val, dx, lenxp, j)

            if j == -1:
                dres.flat[i] = lval
            elif j == lenxp:
                dres.flat[i] = rval
            elif j == lenxp - 1:
                dres.flat[i] = dy[j]
            else:
                if slopes.size:
                    slope = slopes[j]
                else:
                    slope = (dy[j + 1] - dy[j]) / (dx[j + 1] - dx[j])

                dres.flat[i] = slope * (x_val - dx[j]) + dy[j]

                # NOTE: this is in master but not in any released
                # version of 1.16.x yet...
                #
                # If we get nan in one direction, try the other
                # if np.isnan(dres.flat[i]):
                #     dres.flat[i] = slope * (x_val - dx[j + 1]) + dy[j + 1]
                #
                #     if np.isnan(dres.flat[i]) and dy[j] == dy[j + 1]:
                #         dres.flat[i] = dy[j]

    return dres