That's great for continuing from the last checkpoint, but, unless I misunderstand you, my intention is different:

Suppose I trained a model for 30k epochs over night, and looking at the graphs, I wish to get back to the 22k'th epoch and retrain it from there differently, while preserving all the history up to that point.
So, I start by cloning the task, and.. what can I do then to "get back" to the previous epoch? This means that I would like all metrics, logs, checkpoints, etc. from the 22k'th epoch forward deleted, and then to use your approach.