Did I find the right examples for you? yes no

All Samples(1)  |  Call(1)  |  Derive(0)  |  Import(0)
eigvals(x[, out])

eigvals on the last two dimension and broadcast to the rest. 
Results in a vector of eigenvalues. 
    "(m,m)->(m),(m,m)" 

src/n/u/numpy-1.8.1/numpy/linalg/linalg.py   numpy(Download)
        _raise_linalgerror_eigenvalues_nonconvergence)
    signature = 'D->D' if isComplexType(t) else 'd->D'
    w = _umath_linalg.eigvals(a, signature=signature, extobj=extobj)
 
    if not isComplexType(t):