model.py

from keras import backend as K 
K.clear_session() 
latent_dim = 500 

# Encoder 
encoder_inputs = Input(shape=(max_len_text,)) 
enc_emb = Embedding(x_voc_size, latent_dim,trainable=True)(encoder_inputs) 

#LSTM 1 
encoder_lstm1 = LSTM(latent_dim,return_sequences=True,return_state=True) 
encoder_output1, state_h1, state_c1 = encoder_lstm1(enc_emb) 

#LSTM 2 
encoder_lstm2 = LSTM(latent_dim,return_sequences=True,return_state=True) 
encoder_output2, state_h2, state_c2 = encoder_lstm2(encoder_output1) 

#LSTM 3 
encoder_lstm3=LSTM(latent_dim, return_state=True, return_sequences=True) 
encoder_outputs, state_h, state_c= encoder_lstm3(encoder_output2) 

# Set up the decoder. 
decoder_inputs = Input(shape=(None,)) 
dec_emb_layer = Embedding(y_voc_size, latent_dim,trainable=True) 
dec_emb = dec_emb_layer(decoder_inputs) 

#LSTM using encoder_states as initial state
decoder_lstm = LSTM(latent_dim, return_sequences=True, return_state=True) 
decoder_outputs,decoder_fwd_state, decoder_back_state = decoder_lstm(dec_emb,initial_state=[state_h, state_c]) 

#Attention Layer
Attention layer attn_layer = AttentionLayer(name='attention_layer') 
attn_out, attn_states = attn_layer([encoder_outputs, decoder_outputs]) 

# Concat attention output and decoder LSTM output 
decoder_concat_input = Concatenate(axis=-1, name='concat_layer')([decoder_outputs, attn_out])

#Dense layer
decoder_dense = TimeDistributed(Dense(y_voc_size, activation='softmax')) 
decoder_outputs = decoder_dense(decoder_concat_input) 

# Define the model
model = Model([encoder_inputs, decoder_inputs], decoder_outputs) 
model.summary()

I need this can any one help me this