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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 refocus_stack(fieldstack, d, nm, res, method="helmholtz", num_cpus=_cpu_count, copy=True, padding=True):
 """Refocus a stack of 1D or 2D fields Parameters fieldstack : 2d or 3d array Stack of 1D or 2D background corrected electric fields (Ex/BEx). The first axis iterates through the individual fields. d : float Distance to be propagated in pixels (negative for backwards) nm : float Refractive index of medium res : float Wavelenth in pixels method : str Defines the method of propagation; one of - "helmholtz" : the optical transfer function `exp(idkₘ(M-1))` - "fresnel" : paraxial approximation `exp(idk²/kₘ)` num_cpus : str Defines the number of CPUs to be used for refocusing. copy : bool If False, overwrites input stack. padding : bool Perform padding with linear ramp from edge to average to reduce ringing artifacts. .. versionadded:: 0.1.4 Returns ------- Electric field stack at `d`. """


    func = refocus
    names = func.__code__.co_varnames[:func.__code__.co_argcount]

    loc = locals()
    vardict = dict()
    for name in names:
        if name in loc.keys():
            vardict[name] = loc[name]
    # default keyword arguments
    func_def = func.__defaults__[::-1]

    # child processes should only use one cpu
    vardict["num_cpus"] = 1
    vardict["padding"] = padding

    M = fieldstack.shape[0]
    stackargs = list()

    # Create individual arglists for all fields
    for m in range(M):
        kwarg = vardict.copy()
        kwarg["field"] = fieldstack[m]
        # now we turn the kwarg into an arglist
        args = list()
        for i, a in enumerate(names[::-1]):
            # first set default
            if i < len(func_def):
                val = func_def[i]
            if a in kwarg:
                val = kwarg[a]
            args.append(val)
        stackargs.append(args[::-1])

    p = mp.Pool(num_cpus)
    result = p.map_async(_refocus_wrapper, stackargs).get()
    p.close()
    p.terminate()
    p.join()

    if copy:
        data = np.zeros(fieldstack.shape, dtype=result[0].dtype)
    else:
        data = fieldstack

    for m in range(M):
        data[m] = result[m]

    return data