def METHOD_NAME(self):
    L = 3.
    n = 6
    cell = pgto.Cell()
    cell.a = numpy.diag([L,L,L])
    cell.mesh = [n,n,n]
    cell.atom = '''He    2.    2.2      2.
                   He    1.2   1.       1.'''
    cell.basis = {'He': [[0, (1.2, 1)], [1, (0.6, 1)]]}
    cell.verbose = 0
    cell.build(0,0)
    kpts = cell.make_kpts([1,3,1])
    nkpts = len(kpts)
    nao = cell.nao_nr()
    numpy.random.seed(1)
    mo =(numpy.random.random((nkpts,nao,nao)) +
         numpy.random.random((nkpts,nao,nao))*1j)
    with_df = rsdf.RSDF(cell, kpts)
    out = with_df.ao2mo_7d(mo, kpts)
    ref = numpy.empty_like(out)
    kconserv = kpts_helper.get_kconserv(cell, kpts)
    for ki, kj, kk in kpts_helper.loop_kkk(nkpts):
        kl = kconserv[ki, kj, kk]
        tmp = with_df.ao2mo((mo[ki], mo[kj], mo[kk], mo[kl]), kpts[[ki,kj,kk,kl]])
        ref[ki,kj,kk] = tmp.reshape([nao]*4)
    self.assertAlmostEqual(abs(out-ref).max(), 0, 12)