6d506202920745291cc89d1e31de62ddcc3e375a,thinc/layers/lstm.py,,LSTM,#,20

Before Change

    if bi:
        if nO is not None:
            nO //= 2
        model = with_padded(
            clone(
                bidirectional(recurrent(LSTM_step(nO=nO, nI=nI, dropout=dropout))),
                depth
            )
        )
    else:
        model = with_padded(clone(recurrent(LSTM_step(nO=nO, nI=nI, dropout=dropout)), depth))
    return cast(Model[Padded, Padded], model)


@registry.layers("PyTorchLSTM.v0")
def PyTorchLSTM(

After Change

    model = recurrent(LSTM_step(nO=nO, nI=nI, dropout=dropout))
    if bi:
        model = bidirectional(model)
    return clone(model, depth)


@registry.layers("PyTorchLSTM.v0")
def PyTorchLSTM(

Instances