(
        self,
        in_channels: int,
        out_channels: int,
        kernel_size: int,
        stride: int = 1,
        padding: int = 1,
        activation: Callable[[torch.Tensor], torch.Tensor] = F.relu,
        tracking: bool = True,
    )
Instantiating Conv1d class
        Args:
            in_channels (int): the number of channels in the input feature map
            out_channels (int): the number of channels in the output feature emap
            kernel_size (int): the size of the convolving kernel
            stride (int): stride of the convolution. Default: 1
            padding (int): zero-padding added to both sides of the input. Default: 1
            activation (function): activation function. Default: F.relu
            tracking (bool): tracking length of sequence. Default: True
Instantiating Conv1d class
        Args:
            in_channels (int): the number of channels in the input feature map
            out_channels (int): the number of channels in the output feature emap
            kernel_size (int): the size of the convolving kernel
            stride (int): stride of the convolution. Default: 1
            padding (int): zero-padding added to both sides of the input. Default: 1
            activation (function): activation function. Default: F.relu
            tracking (bool): tracking length of sequence. Default: True
Instantiating Conv1d class Args : in_channels ( int ) : the number of channels in the input feature map out_channels ( int ) : the number of channels in the output feature emap kernel_size ( int ) : the size of the convolving kernel stride ( int ) : stride of the convolution . Default : 1 padding ( int ) : zero - padding added to both sides of the input . Default : 1 activation ( function ) : activation function . Default : F . relu tracking ( bool ) : tracking length of sequence . Default : True
def __init__(
        self,
        in_channels: int,
        out_channels: int,
        kernel_size: int,
        stride: int = 1,
        padding: int = 1,
        activation: Callable[[torch.Tensor], torch.Tensor] = F.relu,
        tracking: bool = True,
    ) -> None:
        """Instantiating Conv1d class
        Args:
            in_channels (int): the number of channels in the input feature map
            out_channels (int): the number of channels in the output feature emap
            kernel_size (int): the size of the convolving kernel
            stride (int): stride of the convolution. Default: 1
            padding (int): zero-padding added to both sides of the input. Default: 1
            activation (function): activation function. Default: F.relu
            tracking (bool): tracking length of sequence. Default: True
        """
        super(Conv1d, self).__init__()
        self._kernel_size = kernel_size
        self._stride = stride
        self._padding = padding
        self._ops = nn.Conv1d(in_channels, out_channels, kernel_size, stride, padding)
        self._activation = activation
        self._tracking = tracking
def __init__ ( self , in_channels : int , out_channels : int , kernel_size : int , stride : int = 1 , padding : int = 1 , activation : Callable [ [ torch . Tensor ] , torch . Tensor ] = F . relu , tracking : bool = True , ) -> None : super ( Conv1d , self ) . __init__ ( ) self . _kernel_size = kernel_size self . _stride = stride self . _padding = padding self . _ops = nn . Conv1d ( in_channels , out_channels , kernel_size , stride , padding ) self . _activation = activation self . _tracking = tracking