针对上面你给出的建议，修改后的代码是什么样的

以下是针对上面提出的问题进行修正后的代码： ``` from keras.layers import Input, Conv1D, CuDNNGRU, MultiHeadAttention, LayerNormalization, concatenate, Dense, Lambda, add, Activation 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 conv2 = Conv1D(filters=48, kernel_size=6 , strides=1, activation='relu') # for input2 conv2.set_weights(conv1.get_weights()) # set the same weights for conv2 conv1out = conv1(input1) gru1out = CuDNNGRU(64)(conv1out) attention1_output = MultiHeadAttention(num_heads=8, key_dim=64)(gru1out, gru1out) attention1_output = LayerNormalization()(attention1_output + gru1out) input2 = Input(shape=(trainX2.shape[1], trainX2.shape[2])) conv2out = conv2(input2) gru2out = CuDNNGRU(64)(conv2out) attention2_output = MultiHeadAttention(num_heads=8, key_dim=64)(gru2out, gru2out) attention2_output = LayerNormalization()(attention2_output + gru2out) attn_out = concatenate([attention1_output,attention2_output], axis=-1) # specify the concatenation axis output = Dense(trainY.shape[1])(attn_out) #highway highway_window = config.highway_window z = Lambda(lambda k: k[:, -highway_window:, :])(input1) z = Lambda(lambda k: K.permute_dimensions(k, (0, 2, 1)))(z) z = Lambda(lambda k: K.reshape(k, (-1, highway_window*trainX1.shape[2])))(z) z = Dense(trainY.shape[1])(z) output = add([output,z]) output = Activation('tanh')(