TensorFlow GPU Inference Refactoring & Build

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I have an existing GPU-accelerated network component that still compiles, but its inference logic is tangled and hard to maintain. I want the codebase cleaned up and modernised while keeping the same outputs and speed. Here is what I need from you: • Compile the current project on a recent CUDA-enabled Linux environment so we start from a reproducible build. • Re-architect the inference path, shifting it to clear, modular functions that follow TensorFlow best practices. The focus is code refactoring, not new features. • Preserve existing model weights and performance; any change that alters numerical results must be explained. • Deliver a git repo containing the refactored source, build scripts, and a short README that documents dependencies, compile flag...

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