How does NumPy optimize chained indexing performance?

Chained indexing may trigger multiple intermediate views or copies, reducing performance.

Expressions like arr[1:][2:] may create intermediate arrays depending on stride layout. Each step can produce a new view or copy, increasing memory overhead. NumPy does not always fuse chained indexing operations, which can lead to inefficiencies in large datasets.