写出一个基于注意力机制的seq2seq模型具体实现程序，输入维3个不同列表，输出为另一个列表

这是一个基于注意力机制的seq2seq模型的示例实现（使用Python + Keras）：

from keras.layers import Input, LSTM, Dense, Attention
from keras.models import Model

# 设置输入序列的维度
encoder_inputs = Input(shape=(None, input_dim))
# 设置编码器LSTM层
encoder = LSTM(hidden_size, return_state=True)
encoder_outputs, state_h, state_c = encoder(encoder_inputs)
encoder_states = [state_h, state_c]

# 设置解码器LSTM层
decoder_inputs = Input(shape=(None, output_dim))
decoder_lstm = LSTM(hidden_size, return_sequences=True, return_state=True)
decoder_outputs, _, _ = decoder_lstm(decoder_inputs, initial_state=encoder_states)
# 注意力层
attention = Attention()
attention_output = attention([encoder_outputs, decoder_outputs])

# 设置全连接层
decoder_dense = Dense(output_dim, activation='softmax')
decoder_outputs = decoder_dense(attention_output)

# 构建模型
model = Model([encoder_inputs, decoder_inputs], decoder_outputs)
model.compile(optimizer='rmsprop', loss='categorical_crossentropy')

# 训练模型
model.fit([encoder_input_data, decoder_input_data], decoder_target_data, batch_size=batch_size, epochs=epochs)

# 进行预测
encoder_model = Model(encoder_inputs, encoder_states)
decoder_state_input_h = Input(shape=(hidden_size,))
decoder_state_input_c = Input(shape=(hidden_size,))
decoder_states_inputs = [decoder_state_input_h, decoder_state_input_c]
decoder_outputs, state_h, state_c = decoder_lstm(decoder_inputs, initial_state=decoder_states_inputs)
attention_output = attention([encoder_outputs, decoder_outputs])
decoder_outputs = decoder_dense(attention_output)
decoder_model = Model([decoder_inputs] + decoder_states_inputs, [decoder_outputs, state_h, state_c])

请注意，这只是一个示例