How does NumPy handle high-dimensional tensor broadcasting edge cases?

NumPy aligns shapes from right to left and applies strict broadcasting rules for compatibility.

In high-dimensional arrays, NumPy compares dimensions from the trailing axis backward. Dimensions must either match or be 1. If not, broadcasting fails. Internally, NumPy uses virtual stride expansion without copying data, but misalignment can lead to unexpected shape errors.