IMO, shuffle is something they did really fine. Unlike PyTorch datasets, TF allows streaming unbounded data. For something like this work with shuffle, it must

IMO, shuffle is something they did really fine. Unlike PyTorch datasets, TF allows streaming unbounded data. For something like this work with shuffle, it must cache some data before passing it down the pipeline. You specify how much in the argument. This may not seem useful this conventional training, where you usually work with a fixed amount of samples you know beforehand. But there may be cases where this is not true (for instance, in some special cases of augmentation) - the streaming part is useful but then you must use this caching trick. But I agree API naming is not stellar, or at least should come with better documentation.