[internal] Reorder @pytest.mark.large decorator to fix CI

[JyotinderSingh]

Description of the change

The order of the decorators matters in how internal tooling identifies tests

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 @JyotinderSingh, 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 refines the test suite by adjusting the placement of the @pytest.mark.large decorator for a specific test function. This change ensures that the test_quantized_preset_loading_and_saving test is correctly categorized and executed as a 'large' test, which can impact test discovery and execution workflows. The modification is a minor but important adjustment for test suite organization and reliability.

Highlights

• Decorator Reordering: The @pytest.mark.large decorator has been reordered for the test_quantized_preset_loading_and_saving function within keras_hub/src/models/task_test.py.
• Correct Decorator Placement: The decorator was moved from above the @parameterized.named_parameters decorator to directly above the test function definition, ensuring proper application of the 'large' mark.

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[gemini-code-assist]

Code Review

This pull request correctly reorders the @pytest.mark.large decorator in keras_hub/src/models/task_test.py. By placing @pytest.mark.large after @parameterized.named_parameters, it ensures that the large mark is applied to each test case generated by parameterized, rather than just the test generator function. This change aligns with the best practices for using pytest with the parameterized library and ensures the test is configured correctly. The change is approved.

[JyotinderSingh]

The moonshine failure is unrelated to this change, and can be ignored in this case.