How to use the hydrafloods.collection.hfCollection function in hydrafloods

.filter(ee.Filter.listContains('transmitterReceiverPolarisation', 'VV'))

        return

    def _qa(self, img):
        angles = img.select('angle')
        return img.updateMask(angles.lt(45).And(angles.gt(31)))

    def waterMap(self, target_date, **kwargs):
        mapResult = thresholding.bmaxOtsu(
            self.collection, target_date, self.region, **kwargs)

        return mapResult


class Atms(hfCollection):
    def __init__(self, *args, **kwargs):
        super(Atms, self).__init__(*args, useQa=False,**kwargs)

        return

    def _qa(self, img):
        return

    def extract(self, date, region, credentials, outDir='./', gridding_radius=50000):
        files = fetch.atms(credentials, startTime=date,
                           endTime=None, region=region, outDir=outDir)
        geotiffs = list(
            map(lambda x: preprocess.atms(x, gridding_radius), files))
        return geotiffs

    def load(self, files, gcsBucket='', eeAsset=''):

inImage = self.collection.mean().divide(10000)
        if probablistic:
            iters = ee.List.sequence(0, nIters - 1)

            sims = ee.ImageCollection(iters.map(_downscaleWrapper))
            probs = sims.sum().divide(nIters).rename(['probability', 'error'])
            water = probs.select(['probability'], ['water']).gt(probTreshold)
            mapResult = water.addBands(probs.multiply(10000).uint16())

        else:
            mapResult = downscale.bathtub(inImage, hand, permanent)

        return mapResult


class Viirs(hfCollection):
    def __init__(self, *args, assetid='NOAA/VIIRS/001/VNP09GA', **kwargs):
        super(Viirs, self).__init__(*args, assetid=assetid, **kwargs)

        self.collection = self.collection\
            .select(BANDREMAP.get('viirs'), BANDREMAP.get('new'))\
            .map(geeutils.addIndices)

        self.clipToRegion(inplace=True)

        return

    def _qa(self, img):
        cloudBit = int(math.pow(2, 2))
        shadowBit = int(math.pow(2, 3))
        snowBit = int(math.pow(2, 5))

iters = ee.List.sequence(0, nIters - 1)

            sims = ee.ImageCollection(iters.map(_threholdWrapper))
            probs = sims.sum().divide(nIters).rename(['probability'])
            water = probs.select(['probability']).gt(
                probTreshold).rename('water')
            mapResult = water.addBands(probs.multiply(10000).uint16())

        else:
            mapResult = geeutils.globalOtsu(self.downscaled, target_date, self.region, **kwargs)\
                .And(hand.lt(30))

        return mapResult


class Landsat8(hfCollection):
    def __init__(self, *args, assetid='LANDSAT/LC08/C01/T1_SR', **kwargs):
        super(Landsat8, self).__init__(*args, assetid=assetid, **kwargs)

        if self.useQa:
            self.collection = self.collection.map(self._qa)

        self.collection = self.collection\
            .select(BANDREMAP.get('landsat'), BANDREMAP.get('new'))\
            .map(geeutils.addIndices)

        return

    def waterMap(self, target_date, **kwargs):
        mapResult = thresholding.bmaxOtsu(
            self.collection, target_date, self.region, **kwargs)

iters = ee.List.sequence(0, nIters - 1)

            sims = ee.ImageCollection(iters.map(_threholdWrapper))
            probs = sims.sum().divide(nIters).rename(['probability'])
            water = probs.select(['probability']).gt(
                probTreshold).rename('water')
            mapResult = water.addBands(probs.multiply(10000).uint16())

        else:
            mapResult = geeutils.globalOtsu(self.downscaled, target_date, self.region, **kwargs)\
                .And(hand.lt(30))

        return mapResult


class Modis(hfCollection):
    def __init__(self, *args, assetid='MODIS/006/MOD09GA', **kwargs):
        super(Modis, self).__init__(*args, assetid=assetid, **kwargs)

        if self.useQa:
            self.collection = self.collection.map(self._qa)

        self.collection = self.collection\
            .select(BANDREMAP.get('modis'), BANDREMAP.get('new'))\
            .map(geeutils.addIndices)

        self.clipToRegion(inplace=True)

        return

    def _qa(self, img):
        cloudBit = int(math.pow(2, 10))

outCls = self.copy()
            outCls.collection = func(self.collection, **kwargs)
            return outCls

    def merge(self,collection,interleave=False,inplace=False):
        merged = self.collection.merge(collection.collection)
        if inplace:
            self.collection = merged
            return
        else:
            outCls = self.copy()
            outCls.collection = merged
            return outCls


class Sentinel1(hfCollection):
    def __init__(self, *args, assetid='COPERNICUS/S1_GRD', **kwargs):
        super(Sentinel1, self).__init__(*args,assetid=assetid, **kwargs)

        self.collection = self.collection\
            .filter(ee.Filter.listContains('transmitterReceiverPolarisation', 'VV'))

        return

    def _qa(self, img):
        angles = img.select('angle')
        return img.updateMask(angles.lt(45).And(angles.gt(31)))

    def waterMap(self, target_date, **kwargs):
        mapResult = thresholding.bmaxOtsu(
            self.collection, target_date, self.region, **kwargs)

return mapResult

    def _qa(self, img):
        cloudBit = int(math.pow(2, 5))
        shadowBit = int(math.pow(2, 3))
        snowBit = int(math.pow(2, 4))

        qaCloud = img.select('pixel_qa').bitwiseAnd(cloudBit).eq(0)
        qaShadow = img.select('pixel_qa').bitwiseAnd(shadowBit).eq(0)
        qaSnow = img.select('pixel_qa').bitwiseAnd(snowBit).eq(0)
        mask = qaCloud.And(qaShadow).And(qaSnow)
        return img.updateMask(mask)


class Sentinel2(hfCollection):
    def __init__(self, *args, assetid='COPERNICUS/S2_SR', **kwargs):
        super(Sentinel2, self).__init__(*args,assetid=assetid, **kwargs)

        if self.useQa:
            self.collection = self.collection.map(self._qa)

        self.collection = self.collection\
            .select(BANDREMAP.get('sen2'), BANDREMAP.get('new'))\
            .map(geeutils.addIndices)

        return

    def _qa(self, img):
        sclImg = img.select('SCL')  # Scene Classification Map
        mask = sclImg.gte(4).And(sclImg.lte(6))
        return img.updateMask(mask)