In these models, subgraphs are usually represented as high-dimensional feature vectors which are then fed into a linear model to learn the feature weights.
it computes the forward hidden sequence − → h , the backward hidden sequence ← − h and the output sequence v by iterating the forward layer from t = 1 to n, the backward layer from t = n to 1 and then updating the output layer: l is the dimensionality of LSTM hidden vector.