Intuitions for padding model output columns?

[siyueyang]

Thanks for creating this wonderful package!

I have a few questions regrading the functions for deephit model:
https://github.com/havakv/pycox/blob/master/pycox/models/deephit.py

1. Regarding predit_surv in line 155
surv = 1. - cif.sum(0)
why it needs to take the sum for cif? The cumulative sum has already been calculated in cif, and in my view, it should be:
surv = 1 - cif

2. Regarding predict_pmf in line 202
pmf = pad_col(preds.view(preds.size(0), -1)).softmax(1)[:, :-1]
what are the intuitions for padding another column for the model output before softmax?

In the original paper "Continuous and Discrete-Time Survival Prediction with Neural Networks", page 7, it says
Alternatively, one could let φm+1(x) vary freely, something that is quite common in machine learning, but we chose to follow the typical conventions in statistics.

I am confused about this trick. If I took the φm+1(x) very large, then the estimated probability (after applying softmax) for each time interval would be very small, will this affect my predictions?

Any comments would be appreciated! Thanks in advance!