(self, input_size, output_size, kernel_size, pad_type)
Parameters:
            input_size: Input feature size
            output_size: Output feature size
            kernel_size: Kernel width
            pad_type: left -> pad on the left side (to mask future data), 
                      both -> pad on both sides
Parameters:
            input_size: Input feature size
            output_size: Output feature size
            kernel_size: Kernel width
            pad_type: left -> pad on the left side (to mask future data), 
                      both -> pad on both sides
Parameters : input_size : Input feature size output_size : Output feature size kernel_size : Kernel width pad_type : left - > pad on the left side ( to mask future data ) both - > pad on both sides
def __init__(self, input_size, output_size, kernel_size, pad_type):
        """
        Parameters:
            input_size: Input feature size
            output_size: Output feature size
            kernel_size: Kernel width
            pad_type: left -> pad on the left side (to mask future data), 
                      both -> pad on both sides
        """
        super(Conv, self).__init__()
        padding = (kernel_size - 1, 0) if pad_type == 'left' else (kernel_size//2, (kernel_size - 1)//2)
        self.pad = nn.ConstantPad1d(padding, 0)
        self.conv = nn.Conv1d(input_size, output_size, kernel_size=kernel_size, padding=0)
def __init__ ( self , input_size , output_size , kernel_size , pad_type ) : super ( Conv , self ) . __init__ ( ) padding = ( kernel_size - 1 , 0 ) if pad_type == 'left' else ( kernel_size // 2 , ( kernel_size - 1 ) // 2 ) self . pad = nn . ConstantPad1d ( padding , 0 ) self . conv = nn . Conv1d ( input_size , output_size , kernel_size = kernel_size , padding = 0 )