**[Context]**

Book: Deep Learning with PyTorch by Eli Stevens, Luca Antiga, and Thomas Viehmann

Notebook: Chapter 8 Convolutional Neural Network

**[Question]**

Why *sum* is used **twice** when calculating the regularisation terms manually as shown in the code below?

```
def training_loop_l2reg(n_epochs, optimizer, model, loss_fn,
train_loader):
for epoch in range(1, n_epochs + 1):
loss_train = 0.0
for imgs, labels in train_loader:
imgs = imgs.to(device=device)
labels = labels.to(device=device)
outputs = model(imgs)
loss = loss_fn(outputs, labels)
l2_lambda = 0.001
l2_norm = sum(p.pow(2.0).sum()
for p in model.parameters()) # <1>
loss = loss + l2_lambda * l2_norm
optimizer.zero_grad()
loss.backward()
optimizer.step()
loss_train += loss.item()
if epoch == 1 or epoch % 10 == 0:
print('{} Epoch {}, Training loss {}'.format(
datetime.datetime.now(), epoch,
loss_train / len(train_loader)))
```