2022 assignment3 Self_Supervised_Learning.ipynb Baseline: Without Self-Supervised Learning

The instruction under Baseline: Without Self-Supervised Learning said clearly "all weights in the model are trained", but I found something confusing in the corresponding code as follow,

model = Classifier(num_class=len(train_data.classes)).to(device)
for param in model.f.parameters():
    param.requires_grad = False

and

optimizer = optim.Adam(model.fc.parameters(), lr=1e-3, weight_decay=1e-6)

It's obvious that only the weights of the final fc layer are trained, isn't it?

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2022 assignment3 Self_Supervised_Learning.ipynb Baseline: Without Self-Supervised Learning #285