(self, num_classes: int, hidden_dim: int, vocab: Vocab)
Instantiating MaLSTM class

        Args:
            num_classes (int): the number of classes
            hidden_dim (int): the number of features of the hidden states from LSTM
            vocab (model.utils.Vocab): the instance of model.utils.Vocab
Instantiating MaLSTM class
Instantiating MaLSTM class
def __init__(self, num_classes: int, hidden_dim: int, vocab: Vocab) -> None:
        """Instantiating MaLSTM class

        Args:
            num_classes (int): the number of classes
            hidden_dim (int): the number of features of the hidden states from LSTM
            vocab (model.utils.Vocab): the instance of model.utils.Vocab
        """
        super(MaLSTM, self).__init__()
        self._emb = Embedding(
            vocab, padding_idx=1, freeze=False, permuting=False, tracking=True
        )
        self._pipe = Linker(permuting=False)
        self._encoder = LSTMEncoder(
            self._emb._ops.embedding_dim, hidden_dim, using_sequence=False
        )
        self._classifier = nn.Linear(in_features=hidden_dim, out_features=num_classes)
def __init__ ( self , num_classes : int , hidden_dim : int , vocab : Vocab ) -> None : super ( MaLSTM , self ) . __init__ ( ) self . _emb = Embedding ( vocab , padding_idx = 1 , freeze = False , permuting = False , tracking = True ) self . _pipe = Linker ( permuting = False ) self . _encoder = LSTMEncoder ( self . _emb . _ops . embedding_dim , hidden_dim , using_sequence = False ) self . _classifier = nn . Linear ( in_features = hidden_dim , out_features = num_classes )