def sparse_loss(model, lam=0.001):
    """
    自定义稀疏损失函数，lam为稀疏项系数
    """
    loss = 0
    for param in model.parameters():
        loss += torch.sum(torch.abs(param))

    return lam * loss


model = SparseModel()
criterion = nn.MSELoss()
optimizer = torch.optim.SGD(model.parameters(), lr=0.01)

epochs = 200
for epoch in range(epochs):
    for x_batch, y_batch in data_loader:
        optimizer.zero_grad()
        y_pred = model(x_batch)

        loss = criterion(y_pred, y_batch)

        sparsity_loss = sparse_loss(model)

        # 添加稀疏损失
        total_loss = loss + sparsity_loss

        total_loss.backward()
        optimizer.step()

    if (epoch + 1) % 20 == 0:
        print('Epoch[{} / {}], Loss: {:.4f}, SparsityLoss: {:.4f}'.format(epoch + 1, epochs, loss.item(), sparsity_loss.item()))

详细解释一下以上代码，中文解释，和原理