[Python] Fix max_models_per_worker_hint not being enforced in KeyedModelHandler

[schizophrenicmaniac]

The max_models_per_worker_hint limit for a KeyedModelHandler with several model handlers was never actually enforced. run_inference created a new threading.Lock() on every call, so acquiring it always succeeded. Each deserialized copy of the handler (one per DoFn instance, plus new copies when bundle processors are recreated) still had the hint set, so every copy added it to the shared _ModelHandlerManager again. In the repro from the issue, five handler copies raised the limit to 5 and all three models stayed loaded with a hint of 1.

This change makes _ModelHandlerManager.increment_max_models take an optional process_id and count each process only once. KeyedModelHandler.run_inference passes os.getpid(). The pid is computed on the caller's side because the manager is proxied from a separate MultiProcessShared process. Calls without a process id work as before.

I added a unit test for the manager and one based on the repro in the issue (five pickled handler copies sharing one manager, hint of 1). The repro test fails on master and passes with this change. I also added a CHANGES.md entry.

Fixes #40468

[codecov]

Codecov Report

✅ All modified and coverable lines are covered by tests.
✅ Project coverage is 56.23%. Comparing base (76f6e6d) to head (c00266e).
⚠️ Report is 111 commits behind head on master.

Additional details and impacted files

@@             Coverage Diff              @@
##             master   #40500      +/-   ##
============================================
+ Coverage     56.16%   56.23%   +0.06%     
  Complexity     2288     2288              
============================================
  Files          1124     1124              
  Lines        178336   178362      +26     
  Branches       1489     1489              
============================================
+ Hits         100170   100300     +130     
+ Misses        75645    75541     -104     
  Partials       2521     2521              

Flag	Coverage Δ
python	79.68% <100.00%> (+0.13%)	⬆️

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[github-actions]

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R: @shunping for label python.

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