How to use the nlpaug.util.WarningMessage function in nlpaug

def __init__(self, sampling_rate, duration=3, direction='random',
                 shift_max=3, shift_direction='both',
                 name='Shift_Aug', verbose=0):
        super().__init__(
            action=Action.SUBSTITUTE, name=name, device='cpu', verbose=verbose)

        if shift_direction != 'both':
            print(WarningMessage.DEPRECATED.format('shift_direction', '0.0.12', 'direction'))
            direction = shift_direction
        if shift_max != 3:
            print(WarningMessage.DEPRECATED.format('shift_max', '0.0.12', 'duration'))
            duration = shift_max

        self.model = self.get_model(sampling_rate, duration, direction)

def _get_aug_idxes(self, tokens):
        aug_cnt = self.generate_aug_cnt(len(tokens))
        word_idxes = self.pre_skip_aug(tokens, tuple_idx=0)
        word_idxes = self.skip_aug(word_idxes, tokens)
        if len(word_idxes) == 0:
            if self.verbose > 0:
                exception = WarningException(name=WarningName.OUT_OF_VOCABULARY,
                                             code=WarningCode.WARNING_CODE_002, msg=WarningMessage.NO_WORD)
                exception.output()
            return None
        if len(word_idxes) < aug_cnt:
            aug_cnt = len(word_idxes)
        aug_idexes = self.sample(word_idxes, aug_cnt)
        return aug_idexes

def _validate_augment(cls, data):
        if data is None or len(data) == 0:
            return [WarningException(name=WarningName.INPUT_VALIDATION_WARNING,
                                     code=WarningCode.WARNING_CODE_001, msg=WarningMessage.LENGTH_IS_ZERO)]

        return []

def _get_aug_idxes(self, tokens):
        aug_cnt = self.generate_aug_cnt(len(tokens))
        word_idxes = self.pre_skip_aug(tokens)
        word_idxes = self.skip_aug(word_idxes, tokens)

        if len(word_idxes) == 0:
            if self.verbose > 0:
                exception = WarningException(name=WarningName.OUT_OF_VOCABULARY,
                                             code=WarningCode.WARNING_CODE_002, msg=WarningMessage.NO_WORD)
                exception.output()
            return None
        if len(word_idxes) < aug_cnt:
            aug_cnt = len(word_idxes)

        aug_probs = self.model.cal_tfidf(word_idxes, tokens)
        aug_idxes = []

        # It is possible that no token is picked. So re-try
        retry_cnt = 3
        possible_idxes = word_idxes.copy()
        for _ in range(retry_cnt):
            for i, p in zip(possible_idxes, aug_probs):
                if self.prob() < p:
                    aug_idxes.append(i)
                    possible_idxes.remove(i)

def _get_aug_idxes(self, tokens):
        aug_cnt = self.generate_aug_cnt(len(tokens))
        word_idxes = []
        for i, t in enumerate(tokens):
            token = t
            if not self.case_sensitive:
                token = token.lower()
            if token in self.stopwords:
                word_idxes.append(i)

        word_idxes = self.skip_aug(word_idxes, tokens)
        if len(word_idxes) == 0:
            if self.verbose > 0:
                exception = Warning(name=WarningName.OUT_OF_VOCABULARY,
                                    code=WarningCode.WARNING_CODE_002, msg=WarningMessage.NO_WORD)
                exception.output()
            return None
        if len(word_idxes) < aug_cnt:
            aug_cnt = len(word_idxes)
        aug_idexes = self.sample(word_idxes, aug_cnt)
        return aug_idexes

def __init__(self, zone=(0.2, 0.8), coverage=1.,
                 factor=(0.5, 2), loudness_factor=(0.5, 2), name='Loudness_Aug', verbose=0):
        super().__init__(
            action=Action.SUBSTITUTE, name=name, device='cpu', verbose=verbose)

        if loudness_factor != (0.5, 2):
            print(WarningMessage.DEPRECATED.format('loudness_factor', '0.0.12', 'factor'))
            factor = loudness_factor

        self.model = self.get_model(zone, coverage, factor)