Yes, you're right.
It's a bad example, I tried to simplify it, and I ended up oversimplifying it.
Real-life cases are of course more complicated.
What I wanted to accomplish, is very similar to the `key` argument in sorted/min/max/etc..
let my try and give you an example.

assume we have a complex data type, with a timestamp-signed attribute.
our single item will look as follows:
SingleItem = (unique_timestamp, <mutable_data_structure> )
now assume we have a function "extensive_computation"

def extensive_computation(items: List[SingleItem]):
  # very hard work
  sleep(60)

As developer, I know that the every item has unique timestamp.
So for a list of N timestamp, when they are the same, the result of the computation will be the same.

def item_cache_key(items: List[SingleItem]):
  return (timestamp for timestamp, data in items)

I would like to then create:

@lru_cache(128, key=item_cache_key):
def cache_extensive_computation(items):
  extensive_computation(items)

Does that makes more sense?