def METHOD_NAME(self):
    kpts = numpy.random.random((4,3)) * .25
    kpts[3] = kpts[0]
    kpts[2] = kpts[1]
    with_df = df.DF(cell).set(auxbasis='weigend')
    with_df.linear_dep_threshold = 1e-7
    with_df.kpts = kpts
    mo =(numpy.random.random((nao,nao)) +
         numpy.random.random((nao,nao))*1j)
    eri = with_df.get_eri(kpts).reshape((nao,)*4)
    eri0 = numpy.einsum('pjkl,pi->ijkl', eri , mo.conj())
    eri0 = numpy.einsum('ipkl,pj->ijkl', eri0, mo       )
    eri0 = numpy.einsum('ijpl,pk->ijkl', eri0, mo.conj())
    eri0 = numpy.einsum('ijkp,pl->ijkl', eri0, mo       )
    eri1 = with_df.ao2mo(mo, kpts)
    self.assertAlmostEqual(abs(eri1.reshape(eri0.shape)-eri0).sum(), 0, 9)