Ah ok, sorry it sounded like it was an own implementation.
This should work then:
def apply_weight_norm(m):
    if isinstance(m, nn.Linear):
        m = torch.nn.utils.weight_norm(m, 'weight')
        

model = nn.Sequential(nn.Linear(10, 1),
                      nn.ReLU())

model.apply(apply_weight_norm)
print(model[0].weight_g.shape)