Add tensor row inputs for RowFn

[connortsui20]

Summary

• Tracking Issue: Epic: Row-oriented scalar functions #9128
• Depends on: Replace normalized encoding with explicit normalization #9767

Adds the tensor row types used by RowFn scalar functions without migrating a function in the same layer.

Changes

TensorRow decodes ordinary tensor columns, literal constants, and extension-backed constants into flat typed rows. It is dense-safe because tensor float kernels can consume the stored payload of null rows while RowFn owns output validity.

[codspeed-hq]

Merging this PR will regress 1 benchmark

⚠️ Unknown Walltime execution environment detected

Using the Walltime instrument on standard Hosted Runners will lead to inconsistent data.

For the most accurate results, we recommend using CodSpeed Macro Runners: bare-metal machines fine-tuned for performance measurement consistency.

⚠️ Different runtime environments detected

Some benchmarks with significant performance changes were compared across different runtime environments,
which may affect the accuracy of the results.

Open the report in CodSpeed to investigate

⚡ 2 improved benchmarks
❌ 1 regressed benchmark
✅ 2184 untouched benchmarks
⏩ 218 skipped benchmarks¹

Warning

Please fix the performance issues or acknowledge them on CodSpeed.

Performance Changes

	Mode	Benchmark	BASE	HEAD	Efficiency
❌	Simulation	random_i8[0.5]	67.7 µs	91.4 µs	-25.88%
⚡	WallTime	arrow_checked_add_u32_neon[16384]	20.4 µs	12.8 µs	+59.86%
⚡	Simulation	random_i16[0.8]	93.6 µs	75.3 µs	+24.25%

Tip

Investigate this regression by commenting @codspeedbot fix this regression on this PR, or directly use the CodSpeed MCP with your agent.

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_{Comparing ct/row-fn-tensor-rows (5eba5b2) with ct/tensor-normalization-semantics (fc92123)}

Footnotes

1. 218 benchmarks were skipped, so the baseline results were used instead. If they were deleted from the codebase, click here and archive them to remove them from the performance reports. ↩