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diff --git a/numba/core/extending.py b/numba/core/extending.py
index 9d005fe74..b42442a38 100644
--- a/numba/core/extending.py
+++ b/numba/core/extending.py
@@ -155,8 +155,10 @@ def register_jitable(*args, **kwargs):
     def wrap(fn):
         # It is just a wrapper for @overload
         inline = kwargs.pop('inline', 'never')
+        target = kwargs.pop('target', 'cpu')
 

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from numba import njit, f8
from numba.typed import List
from numba.extending import models, register_model



class Interval(object):
    """
    A half-open interval on the real number line.
    """

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$ PYTHONMALLOC=malloc valgrind-numba python -m numba.runtests numba.tests.test_ufuncs
==7578== Memcheck, a memory error detector
==7578== Copyright (C) 2002-2017, and GNU GPL'd, by Julian Seward et al.
==7578== Using Valgrind-3.17.0 and LibVEX; rerun with -h for copyright info
==7578== Command: python -m numba.runtests numba.tests.test_ufuncs
==7578== 
==7579== Warning: invalid file descriptor 1024 in syscall close()
==7579== Warning: invalid file descriptor 1025 in syscall close()
==7579== Warning: invalid file descriptor 1026 in syscall close()
==7579== Warning: invalid file descriptor 1027 in syscall close()

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# Works in conjunction with https://github.com/numba/numba/pull/7453

from numba import cuda
import asyncio


async def f():
    s1 = cuda.stream()
    s2 = cuda.stream()

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Iterating
Stream <CUDA stream 93950825260944 on <CUDA context c_void_p(93950819544272) of device 0>> done
Stream <CUDA stream 93950825975488 on <CUDA context c_void_p(93950819544272) of device 0>> done

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import math
from numba import cuda, njit, objmode
from time import perf_counter
import numpy as np

import cupy as cp



@njit

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import itertools

import numba as nb
from numba.experimental import jitclass
from typing import List, Tuple, Dict
from heapq import heappush, heappop


# @jitclass
class PurePythonPriorityQueue:

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$ python wagg.py 
Running with 16777216 elements, of which approximately 25.0% are zero

There are 12584753 nonzeroes in:
[0.417022   0.72032449 0.         ... 0.20570723 0.36716537 0.0979951 ]

The kernel found 12584753 elements, resulting in the array:
[0.14349547 0.43006714 0.48695992 ... 0.         0.         0.        ]

Traceback (most recent call last):

gmarkall

import numpy as np
from numba import njit
from time import perf_counter


# From https://en.wikipedia.org/wiki/Fast_Walsh%E2%80%93Hadamard_transform
def fwht(a) -> None:
    """In-place Fast Walsh–Hadamard Transform of array a."""
    h = 1
    while h < len(a):