That works in `numpy`

but **only if the trailing axes have the same dimension**. Here is an example of successfully subtracting a vector from a matrix:

```
In [27]: print m; m.shape
[[ 0 1 2]
[ 3 4 5]
[ 6 7 8]
[ 9 10 11]]
Out[27]: (4, 3)
In [28]: print v; v.shape
[0 1 2]
Out[28]: (3,)
In [29]: m - v
Out[29]:
array([[0, 0, 0],
[3, 3, 3],
[6, 6, 6],
[9, 9, 9]])
```

This worked because the trailing axis of both had the same dimension (3).

In your case, the leading axes had the same dimension. Here is an example, using the same `v`

as above, of how that can be fixed:

```
In [35]: print m; m.shape
[[ 0 1 2 3]
[ 4 5 6 7]
[ 8 9 10 11]]
Out[35]: (3, 4)
In [36]: (m.transpose() - v).transpose()
Out[36]:
array([[0, 1, 2, 3],
[3, 4, 5, 6],
[6, 7, 8, 9]])
```

The rules for broadcasting axes are explained in depth here.