Check handling of convergence criteria in `tao_pounders`

[janosg]

Problem

tao_pounders has three gradient based convergence criteria that all have default values. They are processed in the following code snippet:

# Set user defined convergence tests. Beware that specifying multiple tests could
# overwrite others or lead to unclear behavior.
if stopping_maxiter is not None:
    tao.setConvergenceTest(functools.partial(_max_iters, stopping_maxiter))
elif convergence_gtol_scaled is False and convergence_gtol_abs is False:
    tao.setConvergenceTest(functools.partial(_grtol_conv, convergence_gtol_rel))
elif convergence_gtol_rel is False and convergence_gtol_scaled is False:
    tao.setConvergenceTest(functools.partial(_gatol_conv, convergence_gtol_abs))
elif convergence_gtol_scaled is False:
    tao.setConvergenceTest(
        functools.partial(
            _grtol_gatol_conv,
            convergence_gtol_rel,
            convergence_gtol_abs,
        )
    )

There are two potential problems:

1. The if conditions don't use is True but just a check for truthyness. This could lead to unexpected behavior.
2. It seems like by default all of the convergence criteria are active, even though the comment says that this can lead to unexpected behavior.