该代码的含义def train(model, train_features, train_labels, test_features, test_labels, num_epochs, learning_rate, weight_decay, batch_size):
    train_ls, test_ls = [], []
    train_iteration = gdata.DataLoader(gdata.ArrayDataset(train_features, train_labels), batch_size, shuffle=True)
    trainer = gluon.Trainer(model.collect_params(), 'adam', {'learning_rate': learning_rate, 'wd': weight_decay})
    for epoch in range(num_epochs):
        for X, y in train_iteration:
            with autograd.record():
                l = loss(model(X), y)
            l.backward()
            trainer.step(batch_size)
        train_ls.append(log_rmse(model, train_features, train_labels))
        if test_labels is not None:
            test_ls.append(log_rmse(model, test_features, test_labels))
    
    return train_ls, test_ls