那这段代码要怎么与上面那些代码融合 input1 = Input(shape=(trainX1.shape[1], trainX1.shape[2])) conv1 = Conv1D(filters=48, kernel_size=

您可以将上面的代码与以下代码融合，以构建一个完整的模型： ``` 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)(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) #gru2out = Dropout(config.dropout)(gru2out) attention2_output = MultiHeadAttention(num_heads=8, key_dim=64)(gru2out, gru2out) attention2_output = LayerNormalization()(attention2_output + gru2out) attn_out = concatenate([attention1_output,attention2_output]) output = Dense(trainY.shape[1])(attn_out ) model = Model(inputs=[input1, input2], outputs=output) model.compile(loss='mse', optimizer='adam') model.fit([trainX1, trainX2], trainY, epochs=config.epochs, batch_size=config.batch_size, verbose=2) predictions = predict(model, testX1, testX2) ``` 这个模型使用了两个输入，分别是`input1`和`input2`。其中，`input1`表示用于预测当前时间步长的过去时间步长的数据，`input2`表示用于计算注意力的过去时间步长的数据。模型的输出是一个形状为`(样本数量, 特