编写一段基于TensorFlow的代码:将LSTNet框架中的GRU换成BiGRU，在BiGRU后面加入多头注意力机制

import tensorflow as tf class LSTNet(tf.keras.Model): def __init__(self, input_shape, output_shape, num_time_steps, hidden_size, num_layers, num_heads): super(LSTNet, self).__init__() self.input_shape = input_shape self.output_shape = output_shape self.num_time_steps = num_time_steps self.hidden_size = hidden_size self.num_layers = num_layers self.num_heads = num_heads # Convolutional Layer self.conv1d = tf.keras.layers.Conv1D(filters=32, kernel_size=5, activation='relu', padding='same') # BiGRU Layer self.bigru = tf.keras.layers.Bidirectional(tf.keras.layers.GRU(units=self.hidden_size, return_sequences=True)) # Multi-Head Attention Layer self.multihead_attention = tf.keras.layers.MultiHeadAttention(num_heads=self.num_heads, key_dim=self.hidden_size) # Fully Connected Layers self.fc1 = tf.keras.layers.Dense(units=64, activation='relu') self.fc2 = tf.keras.layers.Dense(units=self.output_shape) def call(self, inputs): # Convolutional Layer x = self.conv1d(inputs) # BiGRU Layer x = self.bigru(x) # Multi-Head Attention Layer x = self.multihead_attention(x, x) # Flatten the output of the Multi-Head Attention Layer x = tf.reshape(x, shape=(-1, self.num_time_steps * self.hidden_size)) # Fully Connected Layers x = self.fc1(x) x = self.fc2(x) return x