This code snippet works as expected in Python, but fails to compile in `@njit`

mode for options other than A (see comments in affected lines):

```
import numpy as np
import numba as nb
points = np.float32([[0, -2, -2, 3, 3], [0.1, -2, -2.1, 2.9, 3.1], [0, -1, -1, 3, 3]])
@nb.njit(fastmath=True)
def cluster(points, dMax):
len = points.shape[0]
dist = np.full((len, len), np.inf, dtype=points.dtype)
for i in range(len):
for j in range(i+1, len):
dist[i, j] = np.max(np.abs(points[i]-points[j]))
dist = dist.ravel() # option A
dist.sort() # option A
# (dist := dist.ravel()).sort() # option B
# dist = np.sort(dist, axis=None) # option C
# (dist := dist.flatten()).sort() # option D
return dist
print(cluster(points, 0.1))
```

Do I have to add type hints, or some Numpy methods are just not compatible with Numba?