//  [python]
def _get_j_pass2(mydf, vG, hermi=1, kpts=numpy.zeros((1,3)), verbose=None):
    log = logger.new_logger(mydf, verbose)
    cell = mydf.cell
    nkpts = len(kpts)
    nao = cell.nao_nr()
    nx, ny, nz = mydf.mesh
    vG = vG.reshape(-1,nx,ny,nz)
    nset = vG.shape[0]

    tasks = getattr(mydf, 'tasks', None)
    if tasks is None:
        mydf.tasks = tasks = multi_grids_tasks(cell, mydf.mesh, log)
        log.debug('Multigrid ntasks %s', len(tasks))

    at_gamma_point = gamma_point(kpts)
    if at_gamma_point:
        vj_kpts = numpy.zeros((nset,nkpts,nao,nao))
    else:
        vj_kpts = numpy.zeros((nset,nkpts,nao,nao), dtype=numpy.complex128)

    for grids_dense, grids_sparse in tasks:
        mesh = grids_dense.mesh
        ngrids = numpy.prod(mesh)
        log.debug('mesh %s', mesh)

        gx = numpy.fft.fftfreq(mesh[0], 1./mesh[0]).astype(numpy.int32)
        gy = numpy.fft.fftfreq(mesh[1], 1./mesh[1]).astype(numpy.int32)
        gz = numpy.fft.fftfreq(mesh[2], 1./mesh[2]).astype(numpy.int32)
        #:sub_vG = vG[:,gx[:,None,None],gy[:,None],gz].reshape(nset,ngrids)
        sub_vG = _take_4d(vG, (None, gx, gy, gz)).reshape(nset,ngrids)

        v_rs = tools.ifft(sub_vG, mesh).reshape(nset,ngrids)
        vR = numpy.asarray(v_rs.real, order='C')
        vI = numpy.asarray(v_rs.imag, order='C')
        ignore_vG_imag = hermi == 1 or abs(vI.sum()) < IMAG_TOL
        if ignore_vG_imag:
            v_rs = vR
        elif vj_kpts.dtype == numpy.double:
            # ensure result complex array if tddft amplitudes are complex while
            # at gamma point
            vj_kpts = vj_kpts.astype(numpy.complex128)

        idx_h = grids_dense.ao_idx
        if grids_sparse is None:
            for ao_h_etc, p0, p1 in mydf.aoR_loop(grids_dense, kpts):
                ao_h = ao_h_etc[0]
                for k in range(nkpts):
                    for i in range(nset):
                        aow = numint._scale_ao(ao_h[k], v_rs[i,p0:p1])
                        vj_sub = lib.dot(ao_h[k].conj().T, aow)
                        vj_kpts[i,k,idx_h[:,None],idx_h] += vj_sub
                ao_h = ao_h_etc = None
        else:
            idx_h = grids_dense.ao_idx
            idx_l = grids_sparse.ao_idx
            # idx_t = numpy.append(idx_h, idx_l)
            naoh = len(idx_h)

            h_cell = grids_dense.cell
            l_cell = grids_sparse.cell
            t_cell = h_cell + l_cell
            t_cell, coeff = t_cell.to_uncontracted_cartesian_basis()
            nshells_h = _pgto_shells(h_cell)
            nshells_t = _pgto_shells(t_cell)

            h_coeff = scipy.linalg.block_diag(*coeff[:h_cell.nbas])
            t_coeff = scipy.linalg.block_diag(*coeff)
            shls_slice = (0, nshells_h, 0, nshells_t)
            vp = eval_mat(t_cell, vR, shls_slice, 1, 0, 'LDA', kpts)
            # Imaginary part may contribute
            if not ignore_vG_imag:
                vpI = eval_mat(t_cell, vI, shls_slice, 1, 0, 'LDA', kpts)
                vp = numpy.asarray(vp) + numpy.asarray(vpI) * 1j
                vpI = None

            vp = lib.einsum('nkpq,pi,qj->nkij', vp, h_coeff, t_coeff)

            vj_kpts[:,:,idx_h[:,None],idx_h] += vp[:,:,:,:naoh]
            vj_kpts[:,:,idx_h[:,None],idx_l] += vp[:,:,:,naoh:]

            if hermi == 1:
                vj_kpts[:,:,idx_l[:,None],idx_h] += \
                        vp[:,:,:,naoh:].transpose(0,1,3,2).conj()
            else:
                l_coeff = scipy.linalg.block_diag(*coeff[h_cell.nbas:])
                shls_slice = (nshells_h, nshells_t, 0, nshells_h)
                vp = eval_mat(t_cell, vR, shls_slice, 1, 0, 'LDA', kpts)
                # Imaginary part may contribute
                if not ignore_vG_imag:
                    vpI = eval_mat(t_cell, vI, shls_slice, 1, 0, 'LDA', kpts)
                    vp = numpy.asarray(vp) + numpy.asarray(vpI) * 1j
                    vpI = None
                vp = lib.einsum('nkpq,pi,qj->nkij', vp, l_coeff, h_coeff)
                vj_kpts[:,:,idx_l[:,None],idx_h] += vp

    return vj_kpts