ponder-lab / Hybridize-Functions-Refactoring / 26304946101
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Extend precision audit to fixtures 83-93 (`random.*` family) (#532)

* Extend precision audit to fixtures 83-93 (`random.*` family)

Cover the `tf.random.*` constructors with the precision-audit helper. All eleven fixtures are IDEAL—Layer 1 (Ariadne) and Layer 2 (Hybridize inference) both match the Python runtime ground truth, no TODO anchors needed.

- **`random.gamma(shape=[10], alpha=[2 floats])`** (83-86): output `(10, 2)` from `shape + broadcast(alpha)`, FLOAT32 default dtype.
- **`random.normal(shape=[4], mean, stddev, tf.float32)`** (87-89): output `(4,)`, FLOAT32 from explicit dtype argument.
- **`random.poisson(shape=[10], lam=[2 floats])`** (90-91): output `(10, 2)` matching `random.gamma`'s broadcasting rule.
- **`random.truncated_normal(shape=[2], ...)`** (92-93): output `(2,)`, FLOAT32 default dtype.

Add the one-arg `testHasLikelyTensorParameterHelper(TensorType expected)` overload back (it was removed when no caller existed); this batch is the first to use it for the symmetric-Layer-1==Layer-2 case.

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>

* Fix Javadoc on `testHasLikelyTensorParameter88` / `89`

Copilot review on #532 caught two pre-existing Javadoc comments that said `random.gamma` but the fixtures actually exercise `random.normal`. The shape assertion (rank-1 with `NumericDim(4)`) and the Python fixtures themselves use `random.normal`; the comment was stale.

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>

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Co-authored-by: Claude Opus 4.7 <noreply@anthropic.com>

1188 of 1645 relevant lines covered (72.22%)

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