How to use the technical.util.resampled_merge function in technical

def test_resampled_merge(testdata_1m_btc):
    resampled = resample_to_interval(testdata_1m_btc, 5)

    merged = resampled_merge(testdata_1m_btc, resampled)

    assert (len(merged) == len(testdata_1m_btc))
    assert "resample_5_open" in merged
    assert "resample_5_close" in merged
    assert "resample_5_low" in merged
    assert "resample_5_high" in merged

    assert "resample_5_date" not in merged
    assert "resample_5_volume" not in merged
    # Verify the assignment goes to the correct candle
    # If resampling to 5m, then the resampled value needs to be on the 5m candle.
    assert sum(merged.loc[merged['date'] == '2017-11-14 22:54:00', 'resample_5_close'].isna()) == 1
    assert sum(merged.loc[merged['date'] == '2017-11-14 22:55:00', 'resample_5_close'].isna()) == 0
    assert sum(merged.loc[merged['date'] == '2017-11-14 22:56:00', 'resample_5_close'].isna()) == 1

def test_resampled_merge_contains_indicator(testdata_1m_btc):
    resampled = resample_to_interval(testdata_1m_btc, 5)
    resampled['cmf'] = chaikin_money_flow(resampled, 5)
    merged = resampled_merge(testdata_1m_btc, resampled)

    print(merged)
    assert "resample_5_cmf" in merged

from technical.util import resampled_merge

        dataframe['sma5'] = ta.SMA(dataframe, timeperiod=5)
        dataframe['sma200'] = ta.SMA(dataframe, timeperiod=200)

        # resample our dataframes
        dataframe_short = resample_to_interval(dataframe, self.get_ticker_indicator() * 2)
        dataframe_long = resample_to_interval(dataframe, self.get_ticker_indicator() * 8)

        # compute our RSI's
        dataframe_short['rsi'] = ta.RSI(dataframe_short, timeperiod=14)
        dataframe_long['rsi'] = ta.RSI(dataframe_long, timeperiod=14)

        # merge dataframe back together
        dataframe = resampled_merge(dataframe, dataframe_short)
        dataframe = resampled_merge(dataframe, dataframe_long)

        dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)

        dataframe.fillna(method='ffill', inplace=True)

        return dataframe

from technical.util import resample_to_interval
        from technical.util import resampled_merge

        dataframe['sma5'] = ta.SMA(dataframe, timeperiod=5)
        dataframe['sma200'] = ta.SMA(dataframe, timeperiod=200)

        # resample our dataframes
        dataframe_short = resample_to_interval(dataframe, self.get_ticker_indicator() * 2)
        dataframe_long = resample_to_interval(dataframe, self.get_ticker_indicator() * 8)

        # compute our RSI's
        dataframe_short['rsi'] = ta.RSI(dataframe_short, timeperiod=14)
        dataframe_long['rsi'] = ta.RSI(dataframe_long, timeperiod=14)

        # merge dataframe back together
        dataframe = resampled_merge(dataframe, dataframe_short)
        dataframe = resampled_merge(dataframe, dataframe_long)

        dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)

        dataframe.fillna(method='ffill', inplace=True)

        return dataframe

def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:

        dataframe['maShort'] = ta.EMA(dataframe, timeperiod=8)
        dataframe['maMedium'] = ta.EMA(dataframe, timeperiod=21)
        ##################################################################################
        # required for graphing
        bollinger = qtpylib.bollinger_bands(dataframe['close'], window=20, stds=2)
        dataframe['bb_lowerband'] = bollinger['lower']
        dataframe['bb_upperband'] = bollinger['upper']
        dataframe['bb_middleband'] = bollinger['mid']

        dataframe_long = resample_to_interval(dataframe, timeframe_to_minutes(self.ticker_interval) * 12)
        dataframe_long['sma'] = ta.SMA(dataframe_long, timeperiod=50, price='close')
        dataframe = resampled_merge(dataframe, dataframe_long, fill_na=True)

        return dataframe