Optimize Scikit-learn model loading by adding Bulk Tree Construction API

[dantegd]

This PR introduces a bulk tree construction API that significantly improves performance when importing scikit-learn RandomForest models into Treelite. In my benchmarks, the new API achieves ~7-10x speedup over the existing node-by-node construction approach of the current sklearn loader.

The current implementation spends significant time in per-node overhead due to:

• Repeated ModelBuilder method calls for each node
• Python-C++ boundary crossing overhead accumulating across millions of nodes
• Memory allocation patterns that don't benefit from bulk operations

This becomes a bottleneck in workflows like cuML's RandomForestClassifier.from_sklearn(), where treelite import time dominates the conversion process.

This PR implements a BulkConstructTree friend function that directly populates the Tree class's internal ContiguousArray members in a single pass, bypassing the ModelBuilder abstraction for sklearn imports.

Initial benchmarks:

Configuration	Total Nodes	Old API (ms)	Bulk API (ms)	Speedup
classifier, 50 trees, depth=10	39,844	13.5	1.8	7.45x
classifier, 100 trees, depth=15	351,826	77.3	10.2	7.54x
classifier, 300 trees, depth=20	2,520,062	544.9	60.7	8.98x
regressor, 100 trees, depth=15	978,436	195.6	18.8	10.42x

[codecov]

Codecov Report

✅ All modified and coverable lines are covered by tests.
✅ Project coverage is 84.83%. Comparing base (3e70b1d) to head (7353713).
⚠️ Report is 1 commits behind head on mainline.

Additional details and impacted files

@@             Coverage Diff              @@
##           mainline     #651      +/-   ##
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+ Coverage     84.38%   84.83%   +0.45%     
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  Files            75       76       +1     
  Lines          6653     6813     +160     
  Branches        543      557      +14     
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+ Hits           5614     5780     +166     
+ Misses         1039     1033       -6     

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[hcho3]

Can we go ahead and simply remove the old sklearn model builder functions? So LoadSKLearnRandomForestRegressorBulk should be simply called LoadSKLearnRandomForestRegressor, etc.

I don't see a good reason to keep the old functions around, if the new functions are equivalent in functionalities but faster.