# Model stacking, what is the input of meta classifier?

I know that by stacking different models among which there has a low correlation can boost the performance of on single model. And I found a picture

In step 7, the $h_j(x_i)$ in new data $x_i^{'}=\{h_1(x_i), h_2(x_i), ..., h_T(x_i)\}$ is the output class label or the probability of model $j$?