feat(core): add pooling model initial support for V1 engine

[pei0033]

🚀 Summary of Changes

• Implemented _pool() method in RBLNModelRunner for pooling model inference based on GPUModelRunner implementation
• Adjusted warmup logic to handle pooling model initialization
• Added comprehensive example scripts for Qwen3 embedding and reranker models

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📌 Related Issues / Tickets

• Resolves Support Pooling model #147

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✅ Type of Change

• ✨ Feature (feature)
• 🧠 Model support (model)
• 🧬 Core engine changes (core)
• 🛠 Bug fix (bug-fix)
• ⚙️ Performance improvement (perf)
• 🔁 Refactor or code cleanup (refactor)
• 📄 Documentation (docs)
• ❓ Other (other): please describe

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🧪 How to Test

For Qwen3 Embedding

1. Run the embedding example: RBLN_PROFILER=0 RBLN_KERNEL_MODE=triton VLLM_RBLN_USE_VLLM_MODEL=1 VLLM_USE_V1=1 python examples/experimental/qwen3_embedding.py
2. Verify that embedding vectors are generated for input texts
3. Expected output: Similarity scores between queries and documents.

For Qwen3 Reranker

1. Run the reranker example: RBLN_PROFILER=0 RBLN_KERNEL_MODE=triton VLLM_RBLN_USE_VLLM_MODEL=1 VLLM_USE_V1=1 python examples/experimental/qwen3_reranker.py
2. Verify that relevance scores are computed for query-document pairs
3. Expected output: Score list showing document relevance.

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📋 Checklist

• PR title follows Conventional Commits format
• This PR is linked to an existing issue
• The test method is described, and the expected result is clearly stated
• Relevant documentation has been updated (if applicable)

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💬 Notes

• The implementation follows the same pattern as upstream vLLM's V1 engine pooling model support
• Warmup logic has been adjusted to properly initialize pooling models
• This is an initial implementation; additional model types (e.g., BERT-based models) may be added in future PRs.
• The pooling function currently runs on CPU without RBLN compilation. Future optimization may include compiling this operation for better performance.