请用python编写在tensorflow框架下的tcn与注意力机制融合进行预测回归的代码

下面是一个完整的代码实现，使用了 TensorFlow 2.x 实现了一个简单的带有注意力机制的 Temporal Convolutional Network（TCN）进行回归预测：

import tensorflow as tf
from tensorflow.keras.layers import Input, Conv1D, Dense, Dropout, Activation, Add, GlobalMaxPool1D
from tensorflow.keras.models import Model
from tensorflow.keras.optimizers import Adam

class TCN(Model):
    def __init__(self, num_channels, kernel_size, dropout):
        super(TCN, self).__init__()
        self.num_channels = num_channels
        self.kernel_size = kernel_size
        self.dropout = dropout
        self.conv1 = Conv1D(self.num_channels, self.kernel_size, padding='causal', activation='relu')
        self.dropout1 = Dropout(self.dropout)
        self.conv2 = Conv1D(self.num_channels, self.kernel_size, padding='causal', activation='relu')
        self.dropout2 = Dropout(self.dropout)
        self.attention = Conv1D(1, 1, activation='softmax', use_bias=False)

    def call(self, inputs):
        x = self.conv1(inputs)
        x = self.dropout1(x)
        x = self.conv2(x)
        x = self.dropout2(x)
        attention_weights = self.attention(x)
        x = x * attention_weights
        return tf.reduce_sum(x, axis=-1)

class TCNWithAttention(Model):
    def __init__(self, num_channels, kernel_size, dropout, output_units):
        super(TCNWithAttention, self).__init__()
        self.tcn = TCN(num_channels, kernel_size, dropout)
        self.fc = Dense(output_units, activation='linear')

    def call(self, inputs):
        x = self.tcn(inputs)
        x = self.fc(x)
        return x

def compile_and_fit(model, X_train, y_train, X_val, y_val, batch_size, epochs):
    model.compile(loss='mean_squared_error', optimizer=Adam(lr=0.001))
    history = model.fit(X_train, y_train, batch_size=batch_size, epochs=epochs,
                        validation_data=(X_val, y_val), verbose=1)
    return history

num_channels = 32
kernel_size = 2
dropout = 0.5
output_units = 1

inputs = Input(shape=(None, 1))
model = TCNWithAttention(num_channels, kernel_size, dropout,