(self, in_channels: int, out_channels: int)
Instantiating ConvolutionLayer class

        Args:
            in_channels (int): the number of channels from input feature map
            out_channels (int): the number of channels from output feature map
Instantiating ConvolutionLayer class
Instantiating ConvolutionLayer class
def __init__(self, in_channels: int, out_channels: int) -> None:
        """Instantiating ConvolutionLayer class

        Args:
            in_channels (int): the number of channels from input feature map
            out_channels (int): the number of channels from output feature map
        """
        super(ConvolutionLayer, self).__init__()
        self._tri_gram = nn.Conv1d(
            in_channels=in_channels, out_channels=out_channels // 3, kernel_size=3
        )
        self._tetra_gram = nn.Conv1d(
            in_channels=in_channels, out_channels=out_channels // 3, kernel_size=4
        )
        self._penta_gram = nn.Conv1d(
            in_channels=in_channels, out_channels=out_channels // 3, kernel_size=5
        )
def __init__ ( self , in_channels : int , out_channels : int ) -> None : super ( ConvolutionLayer , self ) . __init__ ( ) self . _tri_gram = nn . Conv1d ( in_channels = in_channels , out_channels = out_channels // 3 , kernel_size = 3 ) self . _tetra_gram = nn . Conv1d ( in_channels = in_channels , out_channels = out_channels // 3 , kernel_size = 4 ) self . _penta_gram = nn . Conv1d ( in_channels = in_channels , out_channels = out_channels // 3 , kernel_size = 5 )