Explain caching implementation issues causing test failures

[Copilot]

The recent type annotation changes replaced dynamic lru_cache configuration with static @cache decorator on get_factual_value and get_target_value, breaking cache size configuration and causing memory leaks.

Issues identified

• Ignored cache_size parameter: @cache is equivalent to lru_cache(maxsize=None). Tests explicitly using cache_size=0 to prevent memory exhaustion will fail.

• Memory leak: @cache on instance methods holds references to self, preventing garbage collection. The original _setup_caching() avoided this by applying caching dynamically per-instance.

Context

The original implementation applied caching in __init__:

def _setup_caching(self):
    self.get_factual_value = lru_cache(self.cache_size)(self.get_factual_value)
    self.get_target_value = lru_cache(self.cache_size)(self.get_target_value)

This allowed per-instance cache configuration and proper garbage collection, as documented in the code comments referencing https://rednafi.com/python/lru_cache_on_methods/.

The new decorator-based approach:

@cache
def get_factual_value(self, engine: sa.engine.Engine) -> Any:
    ...

Cannot support configurable cache sizes or prevent holding references to constraint instances.

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[codecov]

Codecov Report

✅ All modified and coverable lines are covered by tests.
✅ Project coverage is 37.58%. Comparing base (041ce7f) to head (4328769).
⚠️ Report is 2 commits behind head on ty.

Additional details and impacted files

@@           Coverage Diff           @@
##               ty     #308   +/-   ##
=======================================
  Coverage   37.58%   37.58%           
=======================================
  Files          18       18           
  Lines        2046     2046           
=======================================
  Hits          769      769           
  Misses       1277     1277           

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