How does NumPy handle internal temporary array lifecycle management?

Temporary arrays are created during complex operations and freed when references go out of scope.

During chained operations or dtype conversions, NumPy allocates temporary buffers. These buffers are managed by Python's reference counting system. Once no references remain, memory is released. However, temporary arrays can significantly increase peak memory usage in large computations.