[BUG] Fix RandomState handling in ROCKET and Hydra transformers

[satwiksps]

Reference Issues/PRs

• Fixes [BUG] ROCKET transform random number generation #3212

What does this implement/fix? Explain your changes.

This PR fixes a bug where passing a RandomState object to the random_state parameter in ROCKET-based and Hydra transformers resulted in the seed being ignored (falling back to None) or causing crashes in backend computations.

Changes implemented:

1. Standardised Seed Handling: Updated the _fit methods in Rocket, MiniRocket, MultiRocket, HydraTransformer, and ROCKETGPU. Now uses sklearn.utils.check_random_state to process the input random_state.
2. Deterministic Backend Seeding: Explicitly generates a 32-bit integer seed from the checked RandomState to pass to the underlying computation backends (Numba, PyTorch, and TensorFlow), ensuring reproducibility regardless of whether an int, None, or RandomState object is provided.
3. RocketGPU Stability: Fixed a crash in ROCKETGPU caused by data type mismatches between float32 inputs and float64 kernels/biases generated by numpy. Explicitly casts generated parameters and inputs to float32 to ensure compatibility with TensorFlow's conv1d.
4. Note on Test Updates: The regression tests in test_all_rockets.py and _rockad.py were updated. Since the random seeding logic was corrected, the deterministic stream of random numbers changed, necessitating updates to the hardcoded expected output values.

Does your contribution introduce a new dependency? If yes, which one?

No.

Any other comments?

I have verified these changes with a local test script that confirms:

• Reproducibility when passing identical RandomState objects.
• Reproducibility when passing integer seeds.
• No regressions in default behavior.
• ROCKETGPU no longer crashes on standard inputs.

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