fix: Update Moonshine to support the JAX backend in the keras-nightly version

[harshaljanjani]

Description of the change

Update Moonshine to support the JAX backend in the keras-nightly version.
The T5 fix is more involved; adding the compute_output_shape() method cascades into quantized model build issues. I currently don’t have the bandwidth to address this involved fix.
Feel free to merge this and handle T5 later though!

cc: @divyashreepathihalli @mattdangerw

Checklist

• I have added all the necessary unit tests for my change.
• I have verified that my change does not break existing code and works with all backends (TensorFlow, JAX, and PyTorch).
• My PR is based on the latest changes of the main branch (if unsure, rebase the code).
• I have followed the Keras Hub Model contribution guidelines in making these changes.
• I have followed the Keras Hub API design guidelines in making these changes.
• I have signed the Contributor License Agreement.

[gemini-code-assist]

Summary of Changes

Hello @harshaljanjani, I'm Gemini Code Assist¹! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!

This pull request primarily focuses on enabling the Moonshine model to function correctly with the JAX backend in the keras-nightly environment. The core change involves adding explicit output shape computation for a key internal layer, which is essential for JAX's static graph compilation. This enhancement broadens the compatibility of Moonshine models, though a known, more complex issue related to T5 models is acknowledged and set aside for future work.

Highlights

• JAX Backend Support: The Moonshine model now officially supports the JAX backend when used with keras-nightly.
• Shape Inference Fix: The PositionEmbedding layer within MoonshineBackbone has been updated with a compute_output_shape method to ensure correct shape inference, which is crucial for JAX compatibility.
• T5 Model Deferral: A more involved fix for T5 models, which would address quantized model build issues, has been explicitly deferred to a later pull request.

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[gemini-code-assist]

Code Review

This pull request correctly adds the compute_output_shape method to the Arange layer, which is a necessary update for JAX backend support in keras-nightly. The change is sound. I have provided one review comment suggesting further improvements to the Arange layer to fully align it with the project's style guide for custom layers. This includes adding a docstring, __init__, and get_config method to enhance maintainability and ensure proper serialization.