帮我写一个带有注意力机制的cnn-bigru的代码

我可以回答这个问题。以下是一个带有注意力机制的cnn-bigru的代码示例：

import tensorflow as tf

class Attention(tf.keras.layers.Layer):
    def __init__(self, units):
        super(Attention, self).__init__()
        self.W1 = tf.keras.layers.Dense(units)
        self.W2 = tf.keras.layers.Dense(units)
        self.V = tf.keras.layers.Dense(1)

    def call(self, features, hidden):
        hidden_with_time_axis = tf.expand_dims(hidden, 1)
        score = tf.nn.tanh(self.W1(features) + self.W2(hidden_with_time_axis))
        attention_weights = tf.nn.softmax(self.V(score), axis=1)
        context_vector = attention_weights * features
        context_vector = tf.reduce_sum(context_vector, axis=1)
        return context_vector, attention_weights

class CNNBiGRU(tf.keras.Model):
    def __init__(self, vocab_size, embedding_dim, cnn_filters, cnn_kernel_size, cnn_pool_size, rnn_units, output_dim):
        super(CNNBiGRU, self).__init__()
        self.embedding = tf.keras.layers.Embedding(vocab_size, embedding_dim)
        self.conv1d = tf.keras.layers.Conv1D(filters=cnn_filters, kernel_size=cnn_kernel_size, activation='relu')
        self.max_pooling1d = tf.keras.layers.MaxPooling1D(pool_size=cnn_pool_size)
        self.bigru = tf.keras.layers.Bidirectional(tf.keras.layers.GRU(rnn_units, return_sequences=True))
        self.attention = Attention(rnn_units)
        self.fc = tf.keras.layers.Dense(output_dim, activation='softmax')

    def call(self, inputs):
        x = self.embedding(inputs)
        x = self.conv1d(x)
        x = self.max_pooling1d(x)
        x = self.bigru(x)
        context_vector, attention_weights = self.attention(x, x)
        x = self.fc(context_vector)
        return x

这个模型包括了一个嵌入层、一个一维卷积层、一个最大池化层、一个双向GRU层、一个注意力层和一个全连接层。注意力层使用了一个前馈神经网络来计算注意力权重，然后将注意力权重应用于GRU层的输出，得到一个上下文向量。最后，全连接层将上下文向量映射到输出类别的概率分布。