During training, the model predicts outputs, computes loss, and updates weights.

for x, y in dataloader:
    pred = model(x)
    loss = loss_fn(pred, y)

    optimizer.zero_grad()
    loss.backward()
    optimizer.step()

Each iteration slightly changes the network parameters. Understanding this flow requires tracking state transitions manually.