如何在Tensorflow/TFLearn中通过LSTM同时学习两个序列？

import tensorflow as tf
import tflearn

# 创建输入层
net = tflearn.input_data(shape=[None, sequence_length, input_dim])

# 创建第一个LSTM层
net = tflearn.lstm(net, n_units=hidden_units, return_seq=True)

# 创建第二个LSTM层
net = tflearn.lstm(net, n_units=hidden_units)

# 创建输出层
net = tflearn.fully_connected(net, n_units=output_dim, activation='linear')

# 定义优化器和损失函数
net = tflearn.regression(net, optimizer='adam', loss='mean_square')

# 构建模型
model = tflearn.DNN(net)

# 准备训练数据
X = ...
Y = ...

# 划分训练集和测试集
trainX, testX, trainY, testY = train_test_split(X, Y, test_size=0.2)

# 训练模型
model.fit(trainX, trainY, validation_set=(testX, testY), batch_size=batch_size, n_epoch=num_epochs)

# 使用模型进行预测
predictions = model.predict(testX)