Standard object-oriented programming provides structure for your code. Metaprogramming techniques, however, allow programs to inspect, modify, and even generate code during runtime. This chapter introduces these advanced Python capabilities and relevant aspects of Python's internal workings, specifically focusing on their application in building flexible and extensible machine learning tools.

You will learn how to use decorators to modify function and method behavior non-intrusively, implement descriptors for fine-grained attribute access control, and apply metaclasses to customize the class creation process itself. We will also cover introspection tools for examining objects and code structure dynamically, along with techniques for dynamic code execution. Understanding these concepts provides insight into how many sophisticated ML libraries operate and equips you to create custom frameworks, plugins, and validation systems tailored to specific machine learning problems.