Residual LSTM in Keras

residual_lstm_keras.py

def make_residual_lstm_layers(input, rnn_depth, rnn_dropout):
    """
    The intermediate LSTM layers return sequences, while the last returns a single element.
    The input is also a sequence. In order to match the shape of input and output of the LSTM
    to sum them we can do it only for all layers but the last.
    """
    for i in range(rnn_depth):
        return_sequences = i < rnn_depth - 1
        x_rnn = LSTM(rnn_width, dropout_W=rnn_dropout, dropout_U=rnn_dropout, return_sequences=return_sequences)(input)
        if return_sequences:
            # residual block
            x = merge([x, x_rnn], mode='sum')
        else:
            # last layer does not return sequences and cannot be residual
            x = x_rnn
    return x

Can you help me understand how this is a residual block ?