High-level APIs in deep learning frameworks offer significant convenience by abstracting complex and potentially unstable operations, such as numerical stability, and allowing users to implement models concisely. This abstraction dramatically increases accessibility, enabling individuals without advanced statistical training to utilize deep learning. However, relying solely on high-level APIs presents drawbacks: it discourages the creation of custom, non-standard components and leaves practitioners unequipped to debug edge cases when the framework's abstractions fail. To invent novel architectures that frameworks do not natively support, deep learning practitioners must understand both the fundamental, from-scratch implementations and the concise, framework-based versions.