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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 findpeak_multi(x, y, dy, N, Ntolerance, Nfit=None, curve='Lorentz', return_xfit=False, return_stat=False):
 """Find multiple peaks in the dataset given by vectors x and y. Points are searched for in the dataset where the N points before and after have strictly lower values than them. To get rid of false negatives caused by fluctuations, Ntolerance is introduced. It is the number of outlier points to be tolerated, i.e. points on the left-hand side of the peak where the growing tendency breaks or on the right-hand side where the diminishing tendency breaks. Increasing this number, however gives rise to false positives. Inputs: x, y, dy: vectors defining the data-set. dy can be None. N, Ntolerance: the parameters of the peak-finding routines Nfit: the number of points on the left and on the right of the peak to be used for least squares refinement of the peak positions. curve: the type of the curve to be fitted to the peaks. Can be 'Lorentz' or 'Gauss' return_xfit: if the abscissa used for fitting is to be returned. return_stat: if the fitting statistics is to be returned for each peak. Outputs: position, hwhm, baseline, amplitude, (xfit):
 lists Notes: Peaks are identified where the curve grows N points before and decreases N points after. On noisy curves Ntolerance may improve the results, i.e. decreases the 2*N above mentioned criteria. """

    if Nfit is None:
        Nfit = N
    # find points where the curve grows for N points before them and
    # decreases for N points after them. To accomplish this, we create
    # an indicator array of the sign of the first derivative.
    sgndiff = np.sign(np.diff(y))
    xdiff = x[:-1]  # associate difference values to the lower 'x' value.
    pix = np.arange(len(x) - 1)  # pixel coordinates create an indicator
    # array as the sum of sgndiff shifted left and right.  whenever an
    # element of this is 2*N, it fulfills the criteria above.
    indicator = np.zeros(len(sgndiff) - 2 * N)
    for i in range(2 * N):
        indicator += np.sign(N - i) * sgndiff[i:-2 * N + i]
    # add the last one, since the indexing is different (would be
    # [2*N:0], which is not what we want)
    indicator += -sgndiff[2 * N:]
    # find the positions (indices) of the peak. The strict criteria is
    # relaxed somewhat by using the Ntolerance value. Note the use of
    # 2*Ntolerance, since each outlier point creates two outliers in
    # sgndiff (-1 insted of +1 and vice versa).
    peakpospix = pix[N:-N][indicator >= 2 * N - 2 * Ntolerance]
    ypeak = y[peakpospix]
    # Now refine the found positions by least-squares fitting. But
    # first we have to sort out other non-peaks, i.e. found points
    # which have other found points with higher values in their [-N,N]
    # neighbourhood.
    pos = []; ampl = []; hwhm = []; baseline = []; xfit = []; stat = []
    dy1 = None
    for i in range(len(ypeak)):
        if not [j for j in list(range(i + 1, len(ypeak))) + list(range(0, i)) if abs(peakpospix[j] - peakpospix[i]) <= N and ypeak[i] < ypeak[j]]:
            # only leave maxima.
            idx = peakpospix[i]
            if dy is not None:
                dy1 = dy[(idx - Nfit):(idx + Nfit + 1)]
            xfit_ = x[(idx - Nfit):(idx + Nfit + 1)]
            pos_, hwhm_, baseline_, ampl_, stat_ = findpeak_single(xfit_, y[(idx - Nfit):(idx + Nfit + 1)], dy1, position=x[idx], return_stat=True)
            
            stat.append(stat_)
            xfit.append(xfit_)
            pos.append(pos_)
            ampl.append(ampl_)
            hwhm.append(hwhm_)
            baseline.append(baseline_)
    results = [pos, hwhm, baseline, ampl]
    if return_xfit:
        results.append(xfit)
    if return_stat:
        results.append(stat)
    return tuple(results)