I encounter a strange warning when performing matrix multiplication after QR decomposition in a Numba-accelerated function. For example:

```
# Python 3.10
import numpy as np
from numba import jit
@jit
def qr_check(x):
q,r = np.linalg.qr(x)
return q @ r
x = np.random.rand(3,3)
qr_check(x)
```

Running the above code, I get the following `NumbaPerformanceWarning`

:

```
'@' is faster on contiguous arrays, called on (array(float64, 2d, A), array(float64, 2d, F))
```

I’m not sure what’s going wrong here. I know F is for Fortran, so array `r`

is Fortran-contiguous, but what’s wrong with `q`

? If I include the line `print(q.flags.f_contiguous)`

in the function, I’m told that `q`

is Fortran-contiguous, so I don’t know why I’m getting this warning.