🔒 Fix unsafe JSON deserialization in Parametric models

[derrynknife]

🎯 What: The from_json and from_dict methods in surpyval/univariate/parametric/parametric.py allowed instantiating arbitrary classes and functions from the surpyval module via getattr based on user-provided JSON/dict input.
⚠️ Risk: An attacker could exploit this by providing a crafted JSON file containing an unexpected "distribution" value (e.g., __builtins__ or other internal modules/functions). This unsafe deserialization could lead to unexpected behavior, denial of service, or potentially arbitrary code execution depending on the available targets.
🛡️ Solution: Added a hardcoded whitelist (ALLOWED_DISTRIBUTIONS) containing only valid parametric distributions. The from_dict method now strictly checks the requested distribution against this whitelist and raises a ValueError if the distribution is not recognized, explicitly blocking unsafe deserialization attempts.

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PR created automatically by Jules for task 6319190020759174654 started by @derrynknife

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[chatgpt-codex-connector]

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[chatgpt-codex-connector]

Exclude non-fitters from allowed distribution whitelist

ALLOWED_DISTRIBUTIONS currently includes symbols like MixtureModel, NeverOccurs, and Parametric that are not valid Parametric fitters. In from_dict, these names pass the new whitelist check and then flow into out = cls(dist, ...), where Parametric.__init__ expects dist.k/dist.bounds/dist.param_map; this raises an internal AttributeError instead of the intended ValueError for unrecognized input. A crafted JSON payload using one of these entries can still crash deserialization, so the new validation is not fully enforced for untrusted input.

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