def METHOD_NAME(self, input):
    """
    Args:
        input: torch with shape: batch (b) x sequence(T) x feature (h)
    Returns:
        batch (b) x channel (c) x sequence(T) x feature (h)
    """
    f1 = F.relu(self.linear(input))
    p1 = self.project(f1)
    x = torch.unsqueeze(p1, 1)
    # x: batch (b) x channel (c) x sequence(T) x feature (h)
    x_per = x.permute(0, 3, 2, 1)
    # x_per: batch (b) x feature (h) x sequence(T) x channel (c)
    y = F.pad(x_per, [0, 0, self.lorder - 1, 0])
    out = x_per + self.conv1(y)
    out1 = out.permute(0, 3, 2, 1)
    # out1: batch (b) x channel (c) x sequence(T) x feature (h)
    return input + out1.squeeze()