(
        self,
        vocab: Vocab,
        padding_idx: int = 1,
        freeze: bool = True,
        permuting: bool = True,
        tracking: bool = True,
    )
Instantiating Embedding class
        Args:
            vocab (model.utilsVocab): the instance of model.utils.Vocab
            padding_idx (int): denote padding_idx to padding token
            freeze (bool): freezing weights. Default: False
            permuting (bool): permuting (n, l, c) -> (n, c, l). Default: True
            tracking (bool): tracking length of sequence. Default: True
Instantiating Embedding class
        Args:
            vocab (model.utilsVocab): the instance of model.utils.Vocab
            padding_idx (int): denote padding_idx to padding token
            freeze (bool): freezing weights. Default: False
            permuting (bool): permuting (n, l, c) -> (n, c, l). Default: True
            tracking (bool): tracking length of sequence. Default: True
Instantiating Embedding class Args : vocab ( model . utilsVocab ) : the instance of model . utils . Vocab padding_idx ( int ) : denote padding_idx to padding token freeze ( bool ) : freezing weights . Default : False permuting ( bool ) : permuting ( n l c ) - > ( n c l ) . Default : True tracking ( bool ) : tracking length of sequence . Default : True
def __init__(
        self,
        vocab: Vocab,
        padding_idx: int = 1,
        freeze: bool = True,
        permuting: bool = True,
        tracking: bool = True,
    ) -> None:
        """Instantiating Embedding class
        Args:
            vocab (model.utilsVocab): the instance of model.utils.Vocab
            padding_idx (int): denote padding_idx to padding token
            freeze (bool): freezing weights. Default: False
            permuting (bool): permuting (n, l, c) -> (n, c, l). Default: True
            tracking (bool): tracking length of sequence. Default: True
        """
        super(Embedding, self).__init__()
        self._padding_idx = padding_idx
        self._permuting = permuting
        self._tracking = tracking
        self._ops = nn.Embedding.from_pretrained(
            torch.from_numpy(vocab.embedding),
            freeze=freeze,
            padding_idx=self._padding_idx,
        )
def __init__ ( self , vocab : Vocab , padding_idx : int = 1 , freeze : bool = True , permuting : bool = True , tracking : bool = True , ) -> None : super ( Embedding , self ) . __init__ ( ) self . _padding_idx = padding_idx self . _permuting = permuting self . _tracking = tracking self . _ops = nn . Embedding . from_pretrained ( torch . from_numpy ( vocab . embedding ) , freeze = freeze , padding_idx = self . _padding_idx , )