np.transpose() and np.reshape() combination gives different results in pure numpy and in numba

The following code produces different outputs:

import numpy as np

from numba import njit


@njit
def resh_numba(a):
    res = a.transpose(1, 0, 2)
    res = res.copy().reshape(2, 6)
    return res

x = np.arange(12).reshape(2, 2, 3)

print("numpy")
x_numpy = x.transpose(1, 0, 2).reshape(2, 6)
print(x_numpy)

print("numba:")
x_numba = resh_numba(x)
print(x_numba)

Output:

numpy
[[ 0  1  2  6  7  8]
 [ 3  4  5  9 10 11]]
numba:
[[ 0  4  8  2  6 10]
 [ 1  5  9  3  7 11]]

What is the reason for this? I'm suspecting some order='C' vs order='F' happening somewhere, but I expected both numpy and numba to use order='C' by default everywhere.

Copyright Notice：Content Author:「P. Camilleri」，Reproduced under the CC 4.0 BY-SA copyright license with a link to the original source and this disclaimer.
Link to original article：https://stackoverflow.com/questions/53595093/np-transpose-and-np-reshape-combination-gives-different-results-in-pure-nump