How to use the abipy.flowtk.Flow function in abipy

shiftk = [
        [0.5, 0.5, 0.5],
        [0.5, 0.0, 0.0],
        [0.0, 0.5, 0.0],
        [0.0, 0.0, 0.5],
    ]

    # NSCF run on k-path with large number of bands
    kptbounds = [
        [0.5, 0.0, 0.0],  # L point
        [0.0, 0.0, 0.0],  # Gamma point
        [0.0, 0.5, 0.5],  # X point
    ]

    # Initialize the flow.
    flow = flowtk.Flow(options.workdir, manager=options.manager)

    for nspinor in [1, 2]:
        # Multi will contain two datasets (GS + NSCF) for the given nspinor.
        multi = abilab.MultiDataset(structure=structure, pseudos=pseudos, ndtset=2)

        # Global variables.
        multi.set_vars(
            ecut=20,
            nspinor=nspinor,
            nspden=1 if nspinor == 1 else 4,
            so_psp="*0" if nspinor == 1 else "*1",   # Important!
            #paral_kgb=1,
        )

        nband_occ = num_electrons // 2 if nspinor == 1 else num_electrons
        #print(nband_occ)

Given a initial directory `dirname` containing a node of the `Flow`,
    this function locates the directory of the flow (e.g. the directory with the pickle file)
    and returns the name of the work and/or of the node.

    Return: flowdir, wname, tname

    where flowdir is the directory containing the pickle file,
    wname and tname are the basenames of the work/task.

    If dirname contains the pickle file we have (wname, tname) == (None, None)
    If dirname is a work --> wname is it's basename and tname is None
    If dirname is a task --> os.path.join(flowdir, wname, tname) == task.workdir.
    """
    if dirname is None: dirname = os.getcwd()
    dirname = os.path.abspath(dirname)
    if os.path.exists(os.path.join(dirname, flowtk.Flow.PICKLE_FNAME)):
        return dirname, None, None

    # Handle works or tasks.
    head = dirname
    wname, tname = None, None
    for i in range(2):
        head, tail = os.path.split(head)
        if i == 0: tail_1 = tail
        if os.path.exists(os.path.join(head, flowtk.Flow.PICKLE_FNAME)):
            if i == 0:
                # We have a work: /root/flow_dir/w[num]
                wname = tail
            if i == 1:
                # We have a task: /root/flow_dir/w[num]/t[num]
                wname = tail
                tname = tail_1

options.workdir = os.path.basename(sys.argv[0]).replace(".py", "").replace("run_", "flow_")

    # Initialize structure and pseudos
    structure = abilab.Structure.from_file(abidata.cif_file("si.cif"))
    pseudos = abidata.pseudos("14si.pspnc")

    # Build input for GS calculation.
    gsinp = abilab.AbinitInput(structure, pseudos)
    gsinp.set_vars(ecut=4, nband=4, toldff=1.e-6)

    # This gives ngkpt = 4x4x4 with 4 shifts for the initial unit cell.
    # The k-point sampling will be rescaled when we build the supercell in PhonopyWork.
    gsinp.set_autokmesh(nksmall=4)
    #gsinp.set_vars(ngkpt=[4, 4, 4])

    flow = flowtk.Flow(workdir=options.workdir)

    # Use a 2x2x2 supercell to compute phonons with phonopy
    work = PhonopyWork.from_gs_input(gsinp, scdims=[2, 2, 2])
    flow.register_work(work)

    return flow

def build_flow(options):
    # Set working directory (default is the name of the script with '.py' removed and "run_" replaced by "flow_")
    if not options.workdir:
        options.workdir = os.path.basename(sys.argv[0]).replace(".py", "").replace("run_", "flow_")

    # Get the SCF input (without SOC)
    scf_input = make_scf_input(nspinor=1, usepaw=1)

    # Build the flow.
    from abipy.flowtk.effmass_works import EffMassDFPTWork, EffMassAutoDFPTWork
    flow = flowtk.Flow(workdir=options.workdir, manager=options.manager)

    # Compute effective masses for each k in k0_list.
    # effmass_bands_f90 defines the band range for each k in k0_list
    # Here we are interested in the effective masses at the Gamma point for the valence bands
    effmass_bands_f90 = [1, 4] if scf_input["nspinor"] == 1 else [1, 8]
    work = EffMassDFPTWork.from_scf_input(scf_input, k0_list=(0, 0, 0), effmass_bands_f90=effmass_bands_f90)
    flow.register_work(work)

    # or use this Work to detect band edges automatically but increase ndivsm and decrease tolwfr!
    work = EffMassAutoDFPTWork.from_scf_input(scf_input, ndivsm=5, tolwfr=1e-12)
    flow.register_work(work)

    return flow

def build_flow(options):
    # Working directory (default is the name of the script with '.py' removed and "run_" replaced by "flow_")
    if not options.workdir:
        options.workdir = os.path.basename(sys.argv[0]).replace(".py", "").replace("run_","flow_")

    flow = flowtk.Flow(options.workdir, manager=options.manager)

    pseudos = abidata.pseudos("14si.pspnc")

    # Get the unperturbed structure.
    base_structure = abidata.structure_from_ucell("Si")

    etas = [-.001, 0, +.001]
    ph_displ = np.reshape(np.zeros(3*len(base_structure)), (-1,3))
    ph_displ[0,:] = [+1, 0, 0]
    ph_displ[1,:] = [-1, 0, 0]

    # Build new structures by displacing atoms according to the phonon displacement
    # ph_displ (in cartesian coordinates). The Displacement is normalized so that
    # the maximum atomic diplacement is 1 Angstrom and then multiplied by eta.
    modifier = abilab.StructureModifier(base_structure)

toldff=1e-6,
        tolmxf=1e-5,
        ntime=100,
    )

    # K-points sampling
    shiftk=[
        [0.5,0.5,0.5],
        [0.5,0.0,0.0],
        [0.0,0.5,0.0],
        [0.0,0.0,0.5]
    ]
    relax_inp.set_kmesh(ngkpt=[4, 4, 4], shiftk=shiftk)

    # Initialize the flow
    flow = flowtk.Flow(options.workdir, manager=options.manager)

    # Register the task.
    flow.register_relax_task(relax_inp)

    return flow

options.workdir = os.path.basename(__file__).replace(".py", "").replace("run_", "flow_")

    # Initialize structure and pseudos
    structure = abilab.Structure.from_file(abidata.cif_file("si.cif"))
    pseudos = abidata.pseudos("14si.pspnc")

    # Build input for GS calculation.
    gsinp = abilab.AbinitInput(structure, pseudos)
    gsinp.set_vars(ecut=4, nband=4, toldff=1.e-6)

    # This gives ngkpt = 4x4x4 with 4 shifts for the initial unit cell.
    # The k-point sampling will be rescaled when we build the supercell in PhonopyWork.
    gsinp.set_autokmesh(nksmall=4)
    #gsinp.set_vars(ngkpt=[4, 4, 4])

    flow = flowtk.Flow(workdir=options.workdir)

    # Use a 2x2x2 supercell to compute phonons with phonopy
    work = PhonopyWork.from_gs_input(gsinp, scdims=[2, 2, 2])
    flow.register_work(work)

    return flow

# Initialize pseudos and Structure.
    pseudos = abidata.pseudos("14si.pspnc")
    structure = abilab.Structure.from_file(abidata.cif_file("si.cif"))

    kppa = scf_kppa = 1
    nscf_nband = 6
    nscf_ngkpt = [4, 4, 4]
    nscf_shiftk = [0.1, 0.2, 0.3]
    bs_loband = 2
    bs_nband = nscf_nband
    mbpt_sciss = 0.7 * abilab.units.eV_to_Ha
    mdf_epsinf = 12
    ecuteps = 2
    ecut = 12

    flow = flowtk.Flow(workdir=options.workdir, manager=options.manager)

    # BSE calculation with model dielectric function.
    multi = abilab.bse_with_mdf_inputs(
        structure, pseudos,
        scf_kppa, nscf_nband, nscf_ngkpt, nscf_shiftk,
        ecuteps, bs_loband, bs_nband, mbpt_sciss, mdf_epsinf,
        ecut=ecut,# pawecutdg=None,
        exc_type="TDA", bs_algo="haydock", accuracy="normal", spin_mode="unpolarized",
        smearing=None)
        #smearing="fermi_dirac:0.1 eV", charge=0.0, scf_algorithm=None)

    work = flowtk.BseMdfWork(scf_input=multi[0], nscf_input=multi[1], bse_inputs=multi[2:])

    flow.register_work(work)
    return flow

def build_flow(options):
    # Working directory (default is the name of the script with '.py' removed and "run_" replaced by "flow_")
    if not options.workdir:
        options.workdir = os.path.basename(sys.argv[0]).replace(".py", "").replace("run_", "flow_")

    flow = flowtk.Flow(options.workdir, manager=options.manager)

    # Create the work for the band structure calculation.
    structure = abidata.structure_from_ucell("NiO")
    pseudos = abidata.pseudos("28ni.paw", "8o.2.paw")

    # The code below set up the parameters for the LDA+U calculation in NiO.
    #usepawu   1
    #lpawu   2 -1
    #upawu  8.0 0.0 eV
    #jpawu  0.8 0.0 eV
    usepawu = 1
    u_values = [5.0, 8.0]

    for u in u_values:
        # Apply U-J on Ni only.
        luj_params = LdauParams(usepawu, structure)

[0.0, 0.5, 0.0],
             [0.0, 0.0, 0.5]]

    # Global variables.
    multi.set_vars(ecut=2, paral_kgb=paral_kgb)

    # Dataset 1 (GS run)
    multi[0].set_vars(tolvrs=1e-8, nband=4)
    multi[0].set_kmesh(ngkpt=[4, 4, 4], shiftk=shiftk)

    # NSCF run on k-path with large number of bands
    multi[1].set_vars(iscf=-2, nband=20, tolwfr=1.e-9)
    multi[1].set_kpath(ndivsm=10)

    # Initialize the flow.
    flow = flowtk.Flow(workdir, manager=options.manager, remove=options.remove)

    # GS to get the density + NSCF along the path.
    scf_inp, nscf_inp = multi.split_datasets()
    bands_work = flowtk.BandStructureWork(scf_inp, nscf_inp)
    flow.register_work(bands_work)

    # Build OpticInput used to compute optical properties.
    optic_input = abilab.OpticInput(
        broadening=0.002,          # Value of the smearing factor, in Hartree
        domega=0.0003,             # Frequency mesh.
        maxomega=0.3,
        scissor=0.000,             # Scissor shift if needed, in Hartree
        tolerance=0.002,           # Tolerance on closeness of singularities (in Hartree)
        num_lin_comp=2,            # Number of components of linear optic tensor to be computed
        lin_comp=(11, 33),         # Linear coefficients to be computed (x=1, y=2, z=3)
        num_nonlin_comp=2,         # Number of components of nonlinear optic tensor to be computed