能不能在这段代码的基础上，将GRU换成BiGRU,并在BiGRU后面加入多头注意力机制:def LSTNet(trainX1,trainX2,trainY,config): input1 = Input(shape=(trainX

当然可以，以下是将GRU替换为BiGRU，并在其后添加多头注意力机制的代码示例： ```python from keras.layers import Input, Conv1D, CuDNNGRU, Dropout, concatenate, Dense, Lambda, Activation, add from keras.models import Model import keras.backend as K def LSTNet(trainX1, trainX2, trainY, config): input1 = Input(shape=(trainX1.shape[1], trainX1.shape[2])) conv1 = Conv1D(filters=48, kernel_size=6, strides=1, activation='relu') # for input1 # It's a probelm that I can't find any way to use the same Conv1D layer to train the two inputs, conv2 = Conv1D(filters=48, kernel_size=6 , strides=1, activation='relu') # for input2 conv2.set_weights(conv1.get_weights()) # at least use same weight conv1out = conv1(input1) gru1out = CuDNNGRU(64, return_sequences=True, bidirectional=True)(conv1out) gru1out = Dropout(config.dropout)(gru1out) # Multi-Head Attention layer num_heads = config.num_heads attention = Lambda(lambda x: K.permute_dimensions(x, (0, 2, 1)))(gru1out) attention = Dense(gru1out.shape[-1])(attention) attention = Lambda(lambda x: K.permute_dimensions(x, (0, 2, 1)))(attention) attention = Lambda(lambda x: K.reshape(x, (-1, gru1out.shape[1], num_heads, gru1out.shape[-1]//num_heads)))(attention) attention = Lambda(lambda x: K.permute_dimensions(x, (0, 2, 1, 3)))(attention) attention = Lambda(lambda x: K.batch_dot(x[0], x[1], axes=[3, 2]))([attention, attention]) attention = Lambda(lambda x: K.softmax(x))(attention) attention = Lambda(lambda x: K.batch_dot(x[0], x[1], axes=[3, 2]))([attention, attention]) attention = Lambda(lambda x: K