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/* var SeqToSeq = Class.create();
SeqToSeq.prototype = {
    initialize: function() {
    },

    graph: function (rows) {
       tf.reset_default_graph();
       enc_ip = []
       for (var t in range(0,xseq_len,1)) {
           enc_ip.push(tf.placeholder(shape=[null], dtype=tf.int64, name='ei_{}'.format(t)));
       }
       labels = [];
       for (var t in range(0, yseq_len, 1)) {
           labels.push(tf.placeholder(shape=[null], dtype=tf.int64, name='ei_{}'.format(t)));
       }
       dec_ip = [];
       dec_ip.push(tf.zeros_like(enc_ip[0], dtype=tf.int64, name='GO'));
       labels.pop();
       dec_ip.push(labels);
       keep_prob = tf.placeholder(tf.float32);
       basic_cell = tf.contrib.rnn.core_rnn_cell.DropoutWrapper(
            tf.contrib.rnn.core_rnn_cell.BasicLSTMCell(emb_dim, state_is_Tuple = true), output_keep_prob = keep_prob)
       stacked_lstm = tf.contrib.rnn.core_rnn_cell.MultiRNNCell([basic_Cell*num_layers], state_is_Tuple = true);
       var scope = tf.variable_scope('decoder');
       try {
           decoding = tf.contrib.legacy_seq2seq.embedding_rnn_seq2seq(enc_ip, dec_ip, stacked_lstm, xvocab_size, yvocab_size, emb_dim);
           decode_outputs = decoding.decode_outputs;
           decode_states = decoding.decode_states;
           scope.reuse_variables();
           decodingtest = tf.contrib.legacy_seq2seq.embedding_rnn_seq2seq(enc_ip, dec_ip, stacked_lstm, xvocab_size, yvocab_size, yvocab_size, emb_dim, feed_previous=true);
           decodingtest.decode_outputs_test = decode_outputs_test;
           decodingtest.decode_states_test  = decodingtest.decode_states_test;
       } finally {
            scope.close();
       }
       loss_weights = [];
       for (var label in labels) {
            loss_weights.push(tf.ones_like(label, dtype=tf.float32));
       }
            loss = tf.contrib.legacy_seq2seq.sequence_loss(decode_outputs, labels, loss_weights, yvocab_size)
            train_op =...