写一个基于注意力机制的 BiLSTM 一维时序预测程序的例子，调用你刚刚写的模型

首先，我们需要导入必要的库：

import numpy as np
import tensorflow as tf
from tensorflow.keras.layers import LSTM, Attention, Dense

然后，我们定义一个带注意力机制的 BiLSTM 模型：

class BiLSTMWithAttention(tf.keras.Model):
    def __init__(self, units, output_dim, attention_units):
        super(BiLSTMWithAttention, self).__init__()
        self.output_dim = output_dim
        self.bilstm = tf.keras.layers.Bidirectional(LSTM(units, return_sequences=True))
        self.attention = Attention(attention_units)
        self.fc = Dense(output_dim)

    def call(self, inputs, training=None, mask=None):
        x = self.bilstm(inputs)
        x = self.attention(x)
        x = self.fc(x)
        return x

接下来，我们可以使用这个模型来进行一维时序预测。例如，假设我们有一个时间序列数据 `x`，要预测的标签为 `y`，我们可以这样做：

# 定义模型超参数
units = 64
output_dim = 1
attention_units = 32

# 定义模型
model = BiLSTMWithAttention(units, output_dim, attention_units)

# 定义损失函数和优化器
loss_fn = tf.keras.losses.MeanSquaredError()
optimizer = tf.keras.optimizers.Adam()

# 训练模型
for epoch in range(num_epochs):
    # 计算梯度
    with tf.GradientTape() as tape:
        logits = model(x, training=True)
        loss_value = loss_fn(y, logits)
    grads = tape.gradient(loss_value, model.trainable_variables)
    optimizer.apply_gradients(zip(grads, model.trainable_variables))

# 预测
predictions = model(x, training=False)

完整的例子可以参考：

import numpy as np
import tensorflow as tf