Use NumPy Generator instead of global random state for reproducible stochastic code
Replace np.random.seed() with an explicit numpy.random.Generator to isolate random streams, improve reproducibility, and avoid silent coupling between libraries.
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Replace np.random.seed() with an explicit numpy.random.Generator to isolate random streams, improve reproducibility, and avoid silent coupling between libraries.
When a team uses a repeatable CI/CD pipeline to generate SDKs from an OpenAPI 3.x spec, the mapping of oneOf and anyOf schemas to statically‑typed language models is a frequent source of drift. The OpenAPI Generator fork of Swagger Codegen supports a wide array of target languages, but each language’s template implements its own heuristic for polymorphism (s