How to use the hydrafloods.decorators function in hydrafloods

    @decorators.carryMetadata
    def applyEdge(img):
        # get preliminary water
        binary = img.lt(initialThreshold).rename('binary');

        # get canny edges
        canny = ee.Algorithms.CannyEdgeDetector(binary,canny_threshold,canny_sigma)
        # process canny edges
        connected = canny.mask(canny).lt(canny_lt).connectedPixelCount(connected_pixels, True)
        edges = connected.gte(edge_length)
        edgeBuffer = edges.focal_max(smooth_edges, 'square', 'meters')

        # mask out areas to get histogram for Otsu
        histogram_image = img.updateMask(edgeBuffer)

        histogram =  histogram_image.reduceRegion(ee.Reducer.histogram(255, 2)\
                                    .combine('mean', None, True)\

@decorators.carryMetadata
def addIndices(img):
    ndvi = img.normalizedDifference(['nir','red']).rename('ndvi')
    mndwi = img.normalizedDifference(['green','swir2']).rename('mndwi')
    nwi = img.expression('((b-(n+s+w))/(b+(n+s+w))*100)',{
        'b':img.select('blue'),
        'n':img.select('nir'),
        's':img.select('swir1'),
        'w':img.select('swir2')
    }).rename('nwi')
    aewinsh = img.expression('4.0 * (g-s) - ((0.25*n) + (2.75*w))',{
        'g':img.select('green'),
        's':img.select('swir1'),
        'n':img.select('nir'),
        'w':img.select('swir2')
    }).rename('aewinsh')
    aewish = img.expression('b+2.5*g-1.5*(n+s)-0.25*w',{

    @decorators.carryMetadata
    def apply_starfm(img):
        t = ee.Date(img.get('system:time_start'))

        Tijk = img.select('time').subtract(base.select('time_first'))

        Sijk = img.subtract(slv).abs().convolution(Dijk)\
                .rename(bandList)

        Cijk = Sijk.multiply(Tijk).convolve(Dijk)
        Wijk = one.divide(Cijk).divide(one.divide(Cijk)\
                  .convolve(Dijk))

        nBands = ee.Number(base.bandNames().length())
        expected = ee.Number(len(bandList))

        outImg = ee.Algorithms.If(nBands.neq(expected), None,

    @decorators.carryMetadata
    def applyFilter(img):
        img = geeutils.dbToPower(img)

        # MMSE estimator
        mmseMask = img.gte(a1).Or(img.lte(a2))
        mmseIn = img.updateMask(mmseMask)
        oneImg = ee.Image(1)
        z = mmseIn.reduceNeighborhood(ee.Reducer.mean(), kernel, None, True)
        varz = mmseIn.reduceNeighborhood(ee.Reducer.variance(), kernel)
        varx = (varz.subtract(z.abs().pow(2).multiply(eta))).divide(oneImg.add(eta))
        b = varx.divide(varz)
        mmse = oneImg.subtract(b).multiply(z.abs()).add(b.multiply(mmseIn))

        # workflow
        z99 = ee.Dictionary(img.reduceRegion(
            reducer= ee.Reducer.percentile([99], None, 255, 0.001, 1e6),