Added memory and time optimization for onnx transforms

[abhishek-singh591]

Memory Optimization

Added periodic memory cleanup to FP16ClipTransform and SplitTensorsTransform to reduce memory usage during large tensor processing. Also avoids redundant external data loading when already present.

Time Optimized ONNX Transform via Class Merging and Thread Pooling

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.