LOAD_ATTR Specialization Issue

“LOAD_ATTR does not specialize after replacing an instance's `__dict__` # Bug report ### Bug description: Replacing an instance's `__dict__` prevents `LOAD_ATTR` from specializing on Python 3.14 and current `main`. The same case specializes to `LOAD_ATTR_WITH_HINT` on Python 3.13. ```python import dis class C: pass obj = C() obj.__dict__ = {"x": 1} def f(): return obj.x for _ in range(100): assert f() == 1 dis.dis(f, adaptive=True) ``` Observed result: ```text 3.13: LOAD_ATTR_WITH_HINT 3.14: LOAD_ATTR main: LOAD_ATTR ``` `instance_has_key()` checks `Py_TPFLAGS_INLINE_VALUES` on the type and then searches the type's shared keys. After `__dict__` has been replaced, however, the instance no longer uses its inline values. The attribute is present in the attached dictionary but not necessarily in the shared keys, so specialization is skipped. This appears to have started with gh-123219, which introduced `instance_has_key()` in Python 3.14. I reproduced it on current `main` at `f40043e0953323675843a3c275511596f30c80e9`. This affects Pydantic v2 models because pydantic-core installs validated fields by replacing the model's `__dict__`. In a fresh-process datamodel-code-generator benchmark with 500 JSON Schema definitions, a local prototype reduced median runtime from 354.5 ms to 327.7 ms across 11 alternating samples. ### CPython versions tested on: CPython main branch ### Operating systems tested on: macOS <!-- gh-linked-prs --> ### Linked PRs * gh-155963 <!-- /gh-linked-prs --> ”