Self attention and multi input model in tensorflow

Question

I am trying to use Tensorflow Functional API (https://www.tensorflow.org/guide/keras/functional) to define a multi input neural network and add self attention layer after my embedding layer (https://pypi.org/project/keras-self-attention/). This is my code:

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]) 

However, I get this error:

ValueError: A `Concatenate` layer requires inputs with matching shapes except for the concat axis. Got inputs shapes: [(None, None, 1024), (None, 10)]. 

I only have one sequence in input and I want to apply self attention to it and then concatenate with another input. How can I do that?