That's close, but basically you need to take the square of each element first, e.g. `m * * 2` [0]. This keeps the shape of the matrix while `m.prod()` returns a single number (multiplying each element together).
So it should look something like this: `np.sqrt((m * * 2).sum())`
NotImplementedError: the 'axis' parameter is currently not supported on line 7
I used variations of: np.sqrt(m.sum(m.prod())) np.sqrt(m.sum(m2))
It's been a long time since I've taken linear algebra, so I don't remember some of these operations.