In tensorflow, we can add a L1 or L2 regularizations in the sequential model. I couldn’t find equivalent approach in pytorch. How can we add regularizations to weights in pytorch in the definition of the net:

```
class Net(torch.nn.Module):
def __init__(self, n_feature, n_hidden, n_output):
super(Net, self).__init__()
self.hidden = torch.nn.Linear(n_feature, n_hidden) # hidden layer
""" How to add a L1 regularization after a certain hidden layer?? """
""" OR How to add a L1 regularization after a certain hidden layer?? """
self.predict = torch.nn.Linear(n_hidden, n_output) # output layer
def forward(self, x):
x = F.relu(self.hidden(x)) # activation function for hidden layer
x = self.predict(x) # linear output
return x
net = Net(n_feature=1, n_hidden=10, n_output=1) # define the network
# print(net) # net architecture
optimizer = torch.optim.SGD(net.parameters(), lr=0.2)
loss_func = torch.nn.MSELoss() # this is for regression mean squared loss
```