def METHOD_NAME():
    import pytest
    import numpy as np
    from iminuit import Minuit
    from iminuit.cost import ExtendedUnbinnedNLL
    stats = pytest.importorskip("scipy.stats")
    xmus = 1.0
    xmub = 5.0
    xsigma = 1.0
    ymu = 0.5
    ysigma = 0.2
    ytau = 0.1
    for seed in range(100):
        rng = np.random.default_rng(seed)
        xs = rng.normal(xmus, xsigma, size=33)
        xb = rng.normal(xmub, xsigma, size=66)
        x = np.append(xs, xb)
        def model(x, sig_n, sig_mu, sig_sigma, bkg_n, bkg_tau):
            return sig_n + bkg_n, (
                sig_n * stats.norm.pdf(x, sig_mu, sig_sigma)
                + bkg_n * stats.expon.pdf(x, 0, bkg_tau)
            )
        nll = ExtendedUnbinnedNLL(x, model)
        m = Minuit(nll, sig_n=33, sig_mu=ymu, sig_sigma=ysigma, bkg_n=66, bkg_tau=ytau)
        m.migrad()
        if np.isnan(m.fmin.edm):
            assert not m.valid
            assert m.fmin.is_above_max_edm
            break
    else:
        assert False