如何在Tensorflow训练期间保存两个不同的检查点

import tensorflow as tf

# 定义模型结构
model = ...

# 定义损失函数和优化器
loss = ...
optimizer = ...

# 定义训练过程
def train_step(inputs, labels):
    with tf.GradientTape() as tape:
        predictions = model(inputs)
        current_loss = loss(labels, predictions)
    gradients = tape.gradient(current_loss, model.trainable_variables)
    optimizer.apply_gradients(zip(gradients, model.trainable_variables))
    return current_loss

checkpoint_dir = 'path/to/checkpoints'
checkpoint = tf.train.Checkpoint(model=model, optimizer=optimizer)
manager = tf.train.CheckpointManager(checkpoint, checkpoint_dir, max_to_keep=2)

# 定义训练循环
for epoch in range(num_epochs):
    for batch, (inputs, labels) in enumerate(train_dataset):
        current_loss = train_step(inputs, labels)
    
    # 每个epoch结束时保存检查点
    manager.save()