We will now combine the encoder and decoder to create the sequence-to-sequence model:

def seq2seq_model(input_data, target_en_data, dropout_prob, fr_len, en_len, max_en_len, 
            v_size, rnn_cell_size, n_layers, word2int_en, batch_size):
    input_word_embeddings = tf.Variable(fr_embeddings_matrix, name="input_word_embeddings")
    encoding_embed_input = tf.nn.embedding_lookup(input_word_embeddings, input_data)
    encoding_op, encoding_st = encoding_layer(rnn_cell_size, fr_len, 
                                n_layers, encoding_embed_input, dropout_prob)
    decoding_input = process_encoding_input(target_en_data, word2int_en, batch_size)
    decoding_embed_input = tf.nn.embedding_lookup(en_embeddings_matrix, decoding_input)
    tr_logits, inf_logits = decoding_layer(decoding_embed_input, en_embeddings_matrix,
                            encoding_op,encoding_st, v_size, 
                            fr_len, en_len, max_en_len,
                            rnn_cell_size, word2int_en, 
                            dropout_prob, batch_size,n_layers)
    return tr_logits, inf_logits

The seq2seq_model function combines the source text embeddings, encoder, and decoder and outputs the logits. The input is the French text embeddings, fr_embeddings_matrix. The encoder and decoder layers are created using the functions defined earlier.