numpy broadcasting in np.where

My question is, how do I broadcast values in np.where when using multiple conditions/outputs without having to rely on multiplication?

Input:

import pandas as pd
df = pd.DataFrame({'test':range(0,10)})

   test
0     0
1     1
2     2
3     3
4     4
5     5
6     6
7     7
8     8
9     9

Expected Output:

   test  column1  column2
0     0        2        4
1     1        2        4
2     2        2        4
3     3        2        4
4     4        1        3
5     5        1        3
6     6        1        3
7     7        1        3
8     8        1        3
9     9        1        3

My (working) code:

mask  = df['test'] > 3
m_len = len(mask)

df['column1'], df['column2'] = np.where([mask, mask], [[1]*m_len, [3]*m_len], [[2]*m_len, [4]*m_len])

Question:

Normally np.where() accepts an array and a static value, for example:

np.where(mask, 1, 2) # where mask is a series

My expections where that if I now use this:

np.where([mask, mask], [1, 3], [2, 4])

it would broadcast this values.

But I get the following error:

ValueError: operands could not be broadcast together with shapes (2,10) (2,) (2,) 

Is there a way to broadcast the values without having to use the m_len variable (as shown in my working code)?

Note: I know I can just use np.where multiple times, in multiple lines, but I want to solve it in that one-liner.