Performance on both tier 1 and tier 2 is about 1% slower.
With deferred reference counting, there will be no need for specializing on refcount, so I'm deferring this until deferred reference counting is done.

Earlier results had an issue with thetelco benchmark, probably due to the decimal module falling back to the pure Python version.

Benchmarking on tier 1 shows theslowdown to be in the noise.
The significant results are a speedup of 7% for spectral_norm and a slowdown of 12% for nbody.
This is presumably due to more complete specialization for spectral_norm and additional overhead for the already well specialized nbody code.
We expect the advantage of better specialization to be important in tier 2, but the additional tier 1 overhead to be largely irrelevant there.

Abandoning this as#128722 is handling the same problem and we have a better ideas for reducing refcounting overheadfaster-cpython/ideas#700