How to use the freqtrade.vendor.qtpylib.indicators.crossed_above function in freqtrade

def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        """
        Based on TA indicators. Should be a copy of same method from strategy.
        Must align to populate_indicators in this file.
        Only used when --spaces does not include sell space.
        """
        dataframe.loc[
            (
                (qtpylib.crossed_above(
                    dataframe['macdsignal'], dataframe['macd']
                )) &
                (dataframe['fastd'] > 54)
            ),
            'sell'] = 1

        return dataframe

# GUARDS AND TRENDS
            if 'sell-mfi-enabled' in params and params['sell-mfi-enabled']:
                conditions.append(dataframe['mfi'] > params['sell-mfi-value'])
            if 'sell-fastd-enabled' in params and params['sell-fastd-enabled']:
                conditions.append(dataframe['fastd'] > params['sell-fastd-value'])
            if 'sell-adx-enabled' in params and params['sell-adx-enabled']:
                conditions.append(dataframe['adx'] < params['sell-adx-value'])
            if 'sell-rsi-enabled' in params and params['sell-rsi-enabled']:
                conditions.append(dataframe['rsi'] > params['sell-rsi-value'])

            # TRIGGERS
            if 'sell-trigger' in params:
                if params['sell-trigger'] == 'sell-bb_upper':
                    conditions.append(dataframe['close'] > dataframe['bb_upperband'])
                if params['sell-trigger'] == 'sell-macd_cross_signal':
                    conditions.append(qtpylib.crossed_above(
                        dataframe['macdsignal'], dataframe['macd']
                    ))
                if params['sell-trigger'] == 'sell-sar_reversal':
                    conditions.append(qtpylib.crossed_above(
                        dataframe['sar'], dataframe['close']
                    ))

            if conditions:
                dataframe.loc[
                    reduce(lambda x, y: x & y, conditions),
                    'sell'] = 1

            return dataframe

def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        """
        Based on TA indicators, populates the sell signal for the given dataframe
        :param dataframe: DataFrame
        :return: DataFrame with buy column
        """
        dataframe.loc[
            (
                (dataframe['open'] >= dataframe['ema_high'])
            ) |
            (
                # (dataframe['fastk'] > 70) &
                # (dataframe['fastd'] > 70)
                    (qtpylib.crossed_above(dataframe['fastk'], 70)) |
                    (qtpylib.crossed_above(dataframe['fastd'], 70))
            ),
            'sell'] = 1

        return dataframe

# GUARDS AND TRENDS
            if 'mfi-enabled' in params and params['mfi-enabled']:
                conditions.append(dataframe['mfi'] < params['mfi-value'])
            if 'fastd-enabled' in params and params['fastd-enabled']:
                conditions.append(dataframe['fastd'] < params['fastd-value'])
            if 'adx-enabled' in params and params['adx-enabled']:
                conditions.append(dataframe['adx'] > params['adx-value'])
            if 'rsi-enabled' in params and params['rsi-enabled']:
                conditions.append(dataframe['rsi'] < params['rsi-value'])

            # TRIGGERS
            if 'trigger' in params:
                if params['trigger'] == 'bb_lower':
                    conditions.append(dataframe['close'] < dataframe['bb_lowerband'])
                if params['trigger'] == 'macd_cross_signal':
                    conditions.append(qtpylib.crossed_above(
                        dataframe['macd'], dataframe['macdsignal']
                    ))
                if params['trigger'] == 'sar_reversal':
                    conditions.append(qtpylib.crossed_above(
                        dataframe['close'], dataframe['sar']
                    ))

            if conditions:
                dataframe.loc[
                    reduce(lambda x, y: x & y, conditions),
                    'buy'] = 1

            return dataframe

def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        """
        Based on TA indicators, populates the sell signal for the given dataframe
        :param dataframe: DataFrame
        :return: DataFrame with buy column
        """
        dataframe.loc[
            (
                (dataframe['open'] >= dataframe['ema_high'])
            ) |
            (
                # (dataframe['fastk'] > 70) &
                # (dataframe['fastd'] > 70)
                    (qtpylib.crossed_above(dataframe['fastk'], 70)) |
                    (qtpylib.crossed_above(dataframe['fastd'], 70))
            ),
            'sell'] = 1

        return dataframe

def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        """
        Based on TA indicators. Should be a copy of from strategy
        must align to populate_indicators in this file
        Only used when --spaces does not include sell
        """
        dataframe.loc[
            (
                qtpylib.crossed_above(
                    dataframe[f'maMedium({mediumRangeBegin})'],
                    dataframe[f'maShort({shortRangeBegin})'])
            ),
            'sell'] = 1

        return dataframe

def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe.loc[
            (
                (
                        (dataframe['open'] < dataframe['ema_low']) &
                        (dataframe['adx'] > 30) &
                        (dataframe['mfi'] < 30) &
                        (
                                (dataframe['fastk'] < 30) &
                                (dataframe['fastd'] < 30) &
                                (qtpylib.crossed_above(dataframe['fastk'], dataframe['fastd']))
                        ) &
                        (dataframe['resample_sma'] < dataframe['close'])
                )
                # |
                # # try to get some sure things independent of resample
                # ((dataframe['rsi'] - dataframe['mfi']) < 10) &
                # (dataframe['mfi'] < 30) &
                # (dataframe['cci'] < -200)
            ),
            'buy'] = 1
        return dataframe

conditions.append(dataframe['close'] > dataframe['sar'])
        if params['green_candle']['enabled']:
            conditions.append(dataframe['close'] > dataframe['open'])
        if params['uptrend_sma']['enabled']:
            prevsma = dataframe['sma'].shift(1)
            conditions.append(dataframe['sma'] > prevsma)

        # TRIGGERS
        triggers = {
            'lower_bb': dataframe['tema'] <= dataframe['bb_lowerband'],
            'faststoch10': (crossed_above(dataframe['fastd'], 10.0)),
            'ao_cross_zero': (crossed_above(dataframe['ao'], 0.0)),
            'ema5_cross_ema10': (crossed_above(dataframe['ema5'], dataframe['ema10'])),
            'macd_cross_signal': (crossed_above(dataframe['macd'], dataframe['macdsignal'])),
            'sar_reversal': (crossed_above(dataframe['close'], dataframe['sar'])),
            'stochf_cross': (crossed_above(dataframe['fastk'], dataframe['fastd'])),
            'ht_sine': (crossed_above(dataframe['htleadsine'], dataframe['htsine'])),
        }
        conditions.append(triggers.get(params['trigger']['type']))

        dataframe.loc[
            reduce(lambda x, y: x & y, conditions),
            'buy'] = 1

        return dataframe
    return populate_buy_trend

# GUARDS AND TRENDS
            if 'sell-mfi-enabled' in params and params['sell-mfi-enabled']:
                conditions.append(dataframe['mfi'] > params['sell-mfi-value'])
            if 'sell-fastd-enabled' in params and params['sell-fastd-enabled']:
                conditions.append(dataframe['fastd'] > params['sell-fastd-value'])
            if 'sell-adx-enabled' in params and params['sell-adx-enabled']:
                conditions.append(dataframe['adx'] < params['sell-adx-value'])
            if 'sell-rsi-enabled' in params and params['sell-rsi-enabled']:
                conditions.append(dataframe['rsi'] > params['sell-rsi-value'])

            # TRIGGERS
            if 'sell-trigger' in params:
                if params['sell-trigger'] == 'sell-bb_upper':
                    conditions.append(dataframe['close'] > dataframe['bb_upperband'])
                if params['sell-trigger'] == 'sell-macd_cross_signal':
                    conditions.append(qtpylib.crossed_above(
                        dataframe['macdsignal'], dataframe['macd']
                    ))
                if params['sell-trigger'] == 'sell-sar_reversal':
                    conditions.append(qtpylib.crossed_above(
                        dataframe['sar'], dataframe['close']
                    ))

            if conditions:
                dataframe.loc[
                    reduce(lambda x, y: x & y, conditions),
                    'sell'] = 1

            return dataframe

def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        """
        Based on TA indicators. Should be a copy of same method from strategy.
        Must align to populate_indicators in this file.
        Only used when --spaces does not include sell space.
        """
        dataframe.loc[
            (
                (qtpylib.crossed_above(
                    dataframe['macdsignal'], dataframe['macd']
                )) &
                (dataframe['fastd'] > 54)
            ),
            'sell'] = 1

        return dataframe