def METHOD_NAME(self, x_emb):
    x_emb = x_emb['1D']
    if self.dropout is not None:
        x_emb = self.dropout(x_emb)
    pred_bon = self.pred_bon(x_emb)
    pred_cor = self.pred_cor(x_emb)
    if self.sigmod_normlize:
        bon = torch.sigmoid(pred_bon)
        up = bon[:, 0:1, :] * -0.5 * math.pi
        down = bon[:, 1:, :] * 0.5 * math.pi
        pred_bon = torch.cat([up, down], dim=1)
    return {'pred_bon': pred_bon, 'pred_cor': pred_cor}