请用python写出cnn-attention模型的示例代码

以下是一个简单的CNN-Attention模型的示例代码，用于文本分类任务：

import tensorflow as tf

class CNNAttention(tf.keras.Model):
    def __init__(self, num_classes, embedding_dim, filter_sizes, num_filters, dropout_rate):
        super(CNNAttention, self).__init__()
        self.embedding = tf.keras.layers.Embedding(input_dim=vocab_size, output_dim=embedding_dim)
        self.conv_layers = []
        for filter_size in filter_sizes:
            conv = tf.keras.layers.Conv1D(filters=num_filters, kernel_size=filter_size, activation='relu')
            self.conv_layers.append(conv)
        self.attention = tf.keras.layers.Attention()
        self.dropout = tf.keras.layers.Dropout(dropout_rate)
        self.dense = tf.keras.layers.Dense(num_classes, activation='softmax')

    def call(self, inputs):
        x = self.embedding(inputs)
        x = tf.transpose(x, perm=[0, 2, 1])
        conv_outputs = []
        for conv in self.conv_layers:
            conv_output = conv(x)
            pool_output = tf.keras.layers.GlobalMaxPooling1D()(conv_output)
            conv_outputs.append(pool_output)
        attention_output = self.attention(conv_outputs)
        x = self.dropout(attention_output)
        x = self.dense(x)
        return x

这个模型包含了一个嵌入层、多个卷积层、注意力层、dropout层和全连接层。在 `call` 方法中，我们首先将输入的序列进行嵌入，然后对每个卷积层进行卷积操作并利用全局最大池化层提取特征，最后将多个卷积层的特征输入到注意力层中进行加权融合，再经过dropout和全连接层得到最终的分类结果。