(self, input_dim: int, da: int, r: int)
Instantiating SelfAttention class

        Args:
            input_dim (int): dimension of input, eg) (batch_size, seq_len, input_dim)
            da (int): the number of features in hidden layer from self-attention
            r (int): the number of aspects of self-attention
Instantiating SelfAttention class
Instantiating SelfAttention class
def __init__(self, input_dim: int, da: int, r: int) -> None:
        """Instantiating SelfAttention class

        Args:
            input_dim (int): dimension of input, eg) (batch_size, seq_len, input_dim)
            da (int): the number of features in hidden layer from self-attention
            r (int): the number of aspects of self-attention
        """
        super(SelfAttention, self).__init__()
        self._ws1 = nn.Linear(input_dim, da, bias=False)
        self._ws2 = nn.Linear(da, r, bias=False)
def __init__ ( self , input_dim : int , da : int , r : int ) -> None : super ( SelfAttention , self ) . __init__ ( ) self . _ws1 = nn . Linear ( input_dim , da , bias = False ) self . _ws2 = nn . Linear ( da , r , bias = False )