⚡ Bolt: vectorized BasicEstimator.predict and find_faces

[guesswh0]

💡 What: Vectorized BasicEstimator.predict using the L2 distance expansion formula and optimized BasicEstimator.fit with pre-calculated norms. Also vectorized bounding box area calculation and fixed a metadata slicing bug in FaceEngine.find_faces.

🎯 Why: BasicEstimator.predict used a loop with np.linalg.norm, which is inefficient for large batches. find_faces used a list comprehension for area calculation and had a bug that misaligned extra metadata when limiting detections.

📊 Impact: Achieved ~13x speedup in BasicEstimator.predict (from ~0.25s to ~0.018s for a batch of 500 queries vs 2000 targets). Improved efficiency and correctness in FaceEngine.find_faces.

🔬 Measurement: Verified with benchmark scripts (baseline vs opt) and mock detector script for find_faces. All existing unit tests pass.

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PR created automatically by Jules for task 8092146456918900583 started by @guesswh0

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