I would like to row normalize a fully connected weight using `weight_norm`

. But the problem is it seems NOT to normalize at all.

```
l = nn.Linear(2, 4, bias=False)
# tensor([[ 0.1212, -0.1683],
# [ 0.2399, 0.1841],
# [ 0.2489, 0.6081],
# [ 0.3547, 0.1641]], grad_fn=<MulBackward0>)
m = nn.utils.weight_norm(l)
x = torch.tensor([1., 1.])
l(x)
# tensor([-0.0471, 0.4241, 0.8571, 0.5188], grad_fn=<SqueezeBackward3>)
m(x)
# tensor([-0.0471, 0.4241, 0.8571, 0.5188], grad_fn=<SqueezeBackward3>)
```

`m`

here works exactly the same as `l`

, including the `m.weight`

. I expected the `m`

should be normalized. What should I do then?