The issue described in the article you linked only becomes relevant when you are throwing away data (that you otherwise would have trained on) purely to rectify class imbalance. If you can't train on it anyway due to computational limitations, even if the classes were 50/50 balanced, then there is nothing else to be done.

Of course more data can often lead to better performance and if you find your model to be below par then you may want to explore ways to engineer around whatever computational limitations you are encountering so that you can train on more data. In that case you may want to revisit your approach to rectiifying the class imbalance but don't do it if you don't need to.

Ultimately, anytime you are developing a model and you don't know what to do next, check if the model's performance is acceptable as is. You might not need to do anything.

No it does not. It simply scales the probabilities to either all be closer to 0.5 or all be further from 0.5

Easiest way to find out is to make some calibration plots with your validation set. From there, depending on what the plots look like, there are some things you can do to improve the calibration post training. Look into temperature scaling and Platt scaling.

The same I guess, never used it though

No idea why you are downvoted. High or low level is clearly relative and in this context PyTorch is clearly the low level option.