在tensorflow中有没有像MPI_BARRIER这样的功能呢？

import tensorflow as tf

# 创建分布式训练策略
strategy = tf.distribute.MirroredStrategy()

# 定义模型和优化器
with strategy.scope():
    model = tf.keras.Sequential([...])
    optimizer = tf.keras.optimizers.SGD()

# 定义训练步骤
@tf.function
def train_step(inputs, labels):
    with tf.GradientTape() as tape:
        predictions = model(inputs)
        loss = tf.losses.sparse_categorical_crossentropy(labels, predictions)
    gradients = tape.gradient(loss, model.trainable_variables)
    optimizer.apply_gradients(zip(gradients, model.trainable_variables))
    return loss

# 分布式训练
@tf.function
def distributed_train_step(inputs, labels):
    per_replica_losses = strategy.experimental_run_v2(train_step, args=(inputs, labels))
    loss = strategy.reduce(tf.distribute.ReduceOp.SUM, per_replica_losses, axis=None)
    return loss

# 同步操作
@tf.function
def synchronize():
    strategy.experimental_barrier()

# 执行训练循环
for epoch in range(num_epochs):
    for inputs, labels in train_dataset:
        loss = distributed_train_step(inputs, labels)
        synchronize()
        # 执行其他操作