I don’t know why `nb.njit`

have adverse effect on the performance of my code:

```
lst = [np.array([[1, 2],
[3, 4]]),
np.array([[1, 2, 3],
[4, 5, 6]]),
np.array([[1, 2],
[3, 4],
[5, 6]])]
lst = lst * 10000
def new_(lst):
maxx = 0
maxy = 0
for x in lst:
maxx = max(x.shape[0], maxx)
maxy = max(x.shape[1], maxy)
arr = np.zeros((len(lst), maxx, maxy))
for i in range(len(lst)):
arr[i, :lst[i].shape[0], :lst[i].shape[1]] = lst[i]
return arr
@nb.njit(nb.float64[:, :, ::1](nb.types.List(nb.int_[:, ::1], reflected=True)))
def numba_s2(lst):
maxx = 0
maxy = 0
for x in lst:
maxx = max(x.shape[0], maxx)
maxy = max(x.shape[1], maxy)
arr = np.zeros((len(lst), maxx, maxy))
for i in range(len(lst)):
arr[i, :lst[i].shape[0], :lst[i].shape[1]] = lst[i]
return arr
```

It shows a warning too on both my machine and gloogle colab:

```
/usr/local/lib/python3.7/dist-packages/numba/core/ir_utils.py:2147: NumbaPendingDeprecationWarning:
Encountered the use of a type that is scheduled for deprecation: type 'reflected list' found for argument 'lst' of function 'numba_s2'.
For more information visit https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-reflection-for-list-and-set-types
File "<ipython-input-2-22beb8072bc3>", line 33:
@nb.njit(nb.float64[:, :, ::1](nb.types.List(nb.int_[:, ::1], reflected=True)))
def numba_s2(lst):
^
warnings.warn(NumbaPendingDeprecationWarning(msg, loc=loc))
```

**Is it related to this warning??**

**Can numba be applied efficiently on this code to get better performances?**

**my machine:**

- python 3.10
- numpy 1.22.3
- numba 0.55.2