How does NumPy handle internal error propagation in chained ufunc pipelines?

Errors in ufunc pipelines propagate based on NumPy’s global error handling configuration.

NumPy allows configurable error handling for floating-point issues using np.seterr. In chained ufunc pipelines, errors like divide-by-zero or invalid operations are handled according to this global state. Each ufunc respects these settings, ensuring consistent behavior across chained computations.