Edit: Sorry, I didn't realize it was a 3-d result, not a 4-d result. To get the 3-d one, you would have to reshape once more. And that extra reshape will copy the data.
You can't, you need to tranpose as well:
In [1]: a = np.arange(8)[:,None].repeat(8,axis=1)
In [2]: a
Out[2]:
array([[0, 0, 0, 0, 0, 0, 0, 0],
[1, 1, 1, 1, 1, 1, 1, 1],
[2, 2, 2, 2, 2, 2, 2, 2],
[3, 3, 3, 3, 3, 3, 3, 3],
[4, 4, 4, 4, 4, 4, 4, 4],
[5, 5, 5, 5, 5, 5, 5, 5],
[6, 6, 6, 6, 6, 6, 6, 6],
[7, 7, 7, 7, 7, 7, 7, 7]])
In [3]: b = a.reshape(2,4,2,4)
In [4]: b
Out[4]:
array([[[[0, 0, 0, 0],
[0, 0, 0, 0]],
...
[[7, 7, 7, 7],
[7, 7, 7, 7]]]])
In [5]: b.transpose(0,2,1,3)
Out[5]:
array([[[[0, 0, 0, 0],
[1, 1, 1, 1],
[2, 2, 2, 2],
[3, 3, 3, 3]],
[[0, 0, 0, 0],
[1, 1, 1, 1],
[2, 2, 2, 2],
[3, 3, 3, 3]]],
[[[4, 4, 4, 4],
[5, 5, 5, 5],
[6, 6, 6, 6],
[7, 7, 7, 7]],
[[4, 4, 4, 4],
[5, 5, 5, 5],
[6, 6, 6, 6],
[7, 7, 7, 7]]]])