import tensorflow as tf


        # Split the series because the rnn cell needs time_steps features, each of shape:
        hidden = tf.split(0, config.n_steps/4, hidden)  # (0, 128, [128*batch_size, 32])
        # New hidden's shape: a list of length "time_step" containing tensors of shape [batch_size, n_hidden]

        # Define LSTM cell of first hidden layer:
        lstm_cell = tf.nn.rnn_cell.BasicLSTMCell(config.n_hidden, forget_bias=1.0)

        # Stack two LSTM layers, both layers has the same shape
        lsmt_layers = tf.nn.rnn_cell.MultiRNNCell([lstm_cell] * 2)

        # Get LSTM outputs, the states are internal to the LSTM cells,they are not our attention here
