How does NumPy handle internal memory pinning and buffer lifetime extension?

NumPy extends buffer lifetime using reference counting and shared ownership of memory views.

When multiple arrays or views reference the same buffer, NumPy increments reference counts to prevent premature deallocation. This ensures that even derived views keep underlying memory alive. In cases involving external buffers (like from C extensions), NumPy respects ownership flags to avoid freeing memory it does not own.