请用python编写在tensorflow框架下的tcn与注意力机制融合的代码

下面是一份基于 TensorFlow 2.x 的代码，实现了一个 Time Convolutional Network（TCN）和 Attention Mechanism 的融合：

import tensorflow as tf
from tensorflow.keras import Input, Model
from tensorflow.keras.layers import Conv1D, BatchNormalization, Activation, Attention, Dropout

class TCNAttention(Model):
    def __init__(self, num_classes, input_shape=(None, 64), filters=64, kernel_size=3, dropout=0.5):
        super(TCNAttention, self).__init__()

        self.tcn = [Conv1D(filters, kernel_size, dilation_rate=2**i, padding='causal') for i in range(4)]
        self.tcn = tf.keras.Sequential(self.tcn)
        self.bn = BatchNormalization()
        self.relu = Activation('relu')
        self.attention = Attention()
        self.fc = tf.keras.layers.Dense(num_classes)
        self.dropout = Dropout(dropout)
        
    def call(self, inputs):
        x = self.tcn(inputs)
        x = self.bn(x)
        x = self.relu(x)
        x = self.attention(x)
        x = self.dropout(x)
        x = self.fc(x)

        return x

inputs = Input(shape=(None, 64))
model = TCNAttention(num_classes=10, input_shape=(None, 64))
outputs = model(inputs)
model.summary()

上面的代码定义了一个 `TCNAttention` 模型，其中有四个时间卷积层，分别使用不同的膨胀率，并使用了注意力机制。该模型的概况可以通过调用 `model.summary()` 查看。