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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def autofocus_stack(fieldstack, nm, res, ival, roi=None, metric="average gradient", padding=True, same_dist=False, ret_ds=False, ret_grads=False, num_cpus=_cpu_count, copy=True):
 """Numerical autofocusing of a stack using the Helmholtz equation. Parameters fieldstack : 2d or 3d ndarray Electric field is BG-Corrected, i.e. Field = EX/BEx nm : float Refractive index of medium. res : float Size of wavelength in pixels. ival : tuple of floats Approximate interval to search for optimal focus in px. metric : str see `autofocus_field`. padding : bool Perform padding with linear ramp from edge to average to reduce ringing artifacts. .. versionchanged:: 0.1.4 improved padding value and padding location ret_dopt : bool Return optimized distance and gradient plotting data. same_dist : bool Refocus entire sinogram with one distance. red_ds : bool Return the autofocusing distances in pixels. Defaults to False. If sam_dist is True, still returns autofocusing distances of first pass. The used refocusing distance is the average. red_grads : bool Return the computed gradients as a list. copy : bool If False, overwrites input array. Returns ------- The focused field (and the refocussing distance + data if d is None) """

    dopt = list()
    grad = list()

    M = fieldstack.shape[0]

    # setup arguments
    stackargs = list()
    for s in range(M):
        stackargs.append([fieldstack[s].copy(copy), nm, res, ival,
                          roi, metric, padding, True, True, 1])
    # perform first pass
    p = mp.Pool(num_cpus)
    result = p.map_async(_autofocus_wrapper, stackargs).get()
    p.close()
    p.terminate()
    p.join()
    # result = []
    # for arg in stackargs:
    #    result += _autofocus_wrapper(arg)

    newstack = np.zeros(fieldstack.shape, dtype=fieldstack.dtype)

    for s in range(M):
        field, ds, gs = result[s]
        dopt.append(ds)
        grad.append(gs)
        newstack[s] = field

    # perform second pass if `same_dist` is True
    if same_dist:
        # find average dopt
        davg = np.average(dopt)
        newstack = refocus_stack(fieldstack, davg, nm, res,
                                 num_cpus=num_cpus, copy=copy,
                                 padding=padding)

    ret_list = [newstack]
    if ret_ds:
        ret_list += [dopt]
    if ret_grads:
        ret_list += [grad]

    if len(ret_list) == 1:
        return ret_list[0]
    else:
        return tuple(ret_list)