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TensorFlow best practices for tf.function, GPU memory, and deployment

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# TensorFlow Guide

## Overview

TensorFlow is a production-grade machine learning framework that excels at deployment, distributed training, and hardware acceleration. While PyTorch dominates pure research prototyping, TensorFlow remains the standard in industry ML systems and is heavily used in applied research where models must move from experiment to production.

TensorFlow 2.x unified eager execution with graph-mode performance through `tf.function`, but this hybrid approach introduces subtle pitfalls. Understanding when and how TensorFlow traces functions, manages GPU memory, and distributes computation is essential for writing correct and efficient code.

This guide covers the key patterns that trip up researchers: `tf.function` tracing semantics, GPU memory management, distributed strategies, model export, and the ecosystem of tools (TFX, TensorBoard, TF Serving) that make TensorFlow uniquely powerful for end-to-end ML workflows.

## tf.function: The Critical Abstraction

### How Tracing Works
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