feat: add code samples for preference tuning

[kuirensu-crypto]

Description

Preference Optimization is a new tuning method offered by Vertex Tuning.
Fixes #

Note: Before submitting a pull request, please open an issue for discussion if you are not associated with Google.

Checklist

• I have followed Sample Guidelines from AUTHORING_GUIDE.MD
• README is updated to include all relevant information
• Tests pass: nox -s py-3.9 (see Test Environment Setup)
• Lint pass: nox -s lint (see Test Environment Setup)
• These samples need a new API enabled in testing projects to pass (let us know which ones)
• These samples need a new/updated env vars in testing projects set to pass (let us know which ones)
• This sample adds a new sample directory, and I updated the CODEOWNERS file with the codeowners for this sample
• This sample adds a new Product API, and I updated the Blunderbuss issue/PR auto-assigner with the codeowners for this sample
• Please merge this PR for me once it is approved

[snippet-bot]

Here is the summary of changes.

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• genai/tuning/preference_tuning_job_create.py:17, tag googlegenaisdk_preference_tuning_job_create

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[gemini-code-assist]

Summary of Changes

Hello @kuirensu-crypto, 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 introduces a new code sample for Preference Tuning within the Google GenAI SDK. This is crucial for users looking to leverage Vertex Tuning's advanced optimization methods for generative AI models, specifically demonstrating how to fine-tune models like gemini-2.5-flash using a preference-based approach. The sample provides a practical guide for initiating, monitoring, and retrieving results from such tuning jobs, enhancing the SDK's utility for model customization.

Highlights

• New Preference Tuning Sample: A new Python code sample has been added to demonstrate how to perform Preference Tuning using the Google GenAI SDK, showcasing a new tuning method offered by Vertex Tuning.
• Gemini 2.5 Flash Model Integration: The sample specifically targets the gemini-2.5-flash model, illustrating how to initiate a preference tuning job for this generative AI model.
• Tuning Job Monitoring: The provided code includes logic to poll the status of the tuning job, printing its state until completion, and then outputs details of the tuned model, endpoint, and experiment.

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[gemini-code-assist]

Code Review

This pull request introduces a new code sample for preference tuning. The overall structure is good, but there are several areas for improvement. Most importantly, the new sample is missing tests, which are crucial for ensuring correctness and preventing future regressions. I've also suggested refactoring the code to accept parameters instead of using hardcoded values, which will improve reusability and make it easier for users to run the sample. Additionally, I've pointed out a potential issue with the API version being used and offered some minor suggestions for improving code style and maintainability.