技术上可以使用this solution，但应用到5维空间可能会有点复杂，所以这里是实现，请在使用前自己验证。

# Create a 5-dimensional array as input.
np.random.seed(0)
a = np.random.randint(0, 10, size=(2, 2, 3, 2, 2))
print("a:", a.shape)  # (2, 2, 3, 2, 2)
print(a)
# [[[
#     [[5, 0], [3, 3]],
#     [[7, 9], [3, 5]],
#     [[2, 4], [7, 6]],
# ...

# Flatten all axes except the axis you want to sort on.
# That is, make a 3-dimensional array of (N, sort-axis, M).
b = a.reshape([-1, a.shape[-3], a.shape[-2] * a.shape[-1]])
print("b:", b.shape)  # (4, 3, 4)
print(b)
# [[
#     [5, 0, 3, 3],
#     [7, 9, 3, 5],
#     [2, 4, 7, 6],
# ...

# Then, use lexsort with the bottom axis as sort keys.
idx = np.lexsort([b[..., i] for i in range(b.shape[-1])][::-1])
idx = np.lexsort(np.rollaxis(b, -1)[::-1])  # This is the same as above, but faster.
print("idx:", idx.shape)  # (4, 3)
print(idx)
# [
#     [2, 0, 1],
# ...

# The idx above are the sort order for each block. We can use it like this:
c = np.array([b[i][idx[i]] for i in range(len(b))])
c = b[np.arange(len(b))[:, np.newaxis], idx]  # This is the same as above, but faster.
print("c:", c.shape)  # (4, 3, 4)
print(c)
# [[
#     [2, 4, 7, 6],
#     [5, 0, 3, 3],
#     [7, 9, 3, 5],
# ...

# Restore to the original shape.
d = c.reshape(a.shape)
print("d:", d.shape)  # (2, 2, 3, 2, 2)
print(d)
# [[[
#     [[2, 4], [7, 6]],
#     [[5, 0], [3, 3]],
#     [[7, 9], [3, 5]],
# ...