def lstm_unroll(num_lstm_layer, seq_len, num_hidden, num_label, loss_type=None):
    """
    Creates an unrolled LSTM symbol for inference if loss_type is not specified, and for training
    if loss_type is specified. loss_type must be one of 'ctc' or 'warpctc'

    Parameters
    ----------
    num_lstm_layer: int
    seq_len: int
    num_hidden: int
    num_label: int
    loss_type: str
        'ctc' or 'warpctc'

    Returns
    -------
    mxnet.symbol.symbol.Symbol
    """
    # Create the base (shared between training and inference) and add loss to the end
    pred = _lstm_unroll_base(num_lstm_layer, seq_len, num_hidden)

    if loss_type:
        # Training mode, add loss
        return _add_ctc_loss(pred, seq_len, num_label, loss_type)
    else:
        # Inference mode, add softmax
        return mx.sym.softmax(data=pred, name='softmax')
Creates an unrolled LSTM symbol for inference if loss_type is not specified, and for training
    if loss_type is specified. loss_type must be one of 'ctc' or 'warpctc'

    Parameters
    ----------
    num_lstm_layer: int
    seq_len: int
    num_hidden: int
    num_label: int
    loss_type: str
        'ctc' or 'warpctc'

    Returns
    -------
    mxnet.symbol.symbol.Symbol