我正在尝试使用Tensorflow Functional API（https://www.tensorflow.org/guide/keras/functional）定义多输入神经网络，并在我的嵌入层（https://pypi.org/project/keras-self-attention/）之后添加自我关注层。这是我的代码：
from keras_self_attention import SeqSelfAttention
from tensorflow import keras
Input1 = Input(shape=(120, ),name="Input1")
Input2 = Input(shape=(10, ),name="Input2")
embedding_layer = Embedding(30,5, input_length=120,)(Input1)
lstm_layer = tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(units=512))(embedding_layer)
attention=SeqSelfAttention(attention_activation='sigmoid')(lstm_layer)
merge = concatenate([attention, Input2])
但是，出现此错误：
ValueError: A `Concatenate` layer requires inputs with matching shapes except for the concat axis. Got inputs shapes: [(None, None, 1024), (None, 10)].
我的输入中只有一个序列，我想对此加以注意，然后与另一个输入连接。我该怎么办？