Optimized ONNX Transform via Class Merging and Thread Pooling

[abhishek-singh591]

Optimized ONNX Transform via Class Merging and Thread Pooling

This PR follows up on #539 – Optimized ONNX transform class via multithreading.

It merges the FP16 and Split ONNX transform classes into a single implementation to eliminate redundant tensor loading and iteration. Additionally, the transform logic has been refactored to use a thread pool, replacing the previous sequential loop to parallelize tensor operations.

Performance Benchmarks:-

Model	Original Duration (s)	Optimized Duration (s)
LLaMA 3.1 8B	88.35	58.55
LLaMA 3.1 70B	1029.82	727.37

Note: Thread count is set to os.cpu_count() * 4 to better handle I/O-bound workloads. Performance may vary depending on system hardware and threading capabilities.