How to use the nlpaug.util.Action.SUBSTITUTE function in nlpaug

def test_oov(self):
        unknown_token = 'aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa'
        texts = [
            unknown_token,
            unknown_token + ' the'
        ]

        augmenters = [
            naw.TfIdfAug(model_path=os.environ.get("MODEL_DIR"), action=Action.INSERT),
            naw.TfIdfAug(model_path=os.environ.get("MODEL_DIR"), action=Action.SUBSTITUTE)
        ]

        for aug in augmenters:
            for text in texts:
                self.assertLess(0, len(text))
                augmented_text = aug.augment(text)
                if aug.action == Action.INSERT:
                    self.assertLess(len(text.split(' ')), len(augmented_text.split(' ')))
                    self.assertNotEqual(text, augmented_text)
                elif aug.action == Action.SUBSTITUTE:
                    self.assertEqual(len(text.split(' ')), len(augmented_text.split(' ')))

                    if unknown_token == text:
                        self.assertEqual(text, augmented_text)
                    else:
                        self.assertNotEqual(text, augmented_text)

def test_substitute(self):
        texts = [
            'The quick brown fox jumps over the lazy dog'
        ]

        aug = naw.FasttextAug(
            model_path=os.environ.get("MODEL_DIR") + 'wiki-news-300d-1M.vec',
            action=Action.SUBSTITUTE)

        for text in texts:
            self.assertLess(0, len(text))
            augmented_text = aug.augment(text)

            self.assertNotEqual(text, augmented_text)

        self.assertLess(0, len(texts))

cls.insert_augmenters = [
            naw.Word2vecAug(
                model_path=os.environ.get("MODEL_DIR") + 'GoogleNews-vectors-negative300.bin',
                action=Action.INSERT),
            naw.FasttextAug(
                model_path=os.environ.get("MODEL_DIR") + 'wiki-news-300d-1M.vec',
                action=Action.INSERT),
            naw.GloVeAug(
                model_path=os.environ.get("MODEL_DIR") + 'glove.6B.50d.txt',
                action=Action.INSERT)
        ]

        cls.substitute_augmenters = [
            naw.Word2vecAug(
                model_path=os.environ.get("MODEL_DIR") + 'GoogleNews-vectors-negative300.bin',
                action=Action.SUBSTITUTE),
            naw.FasttextAug(
                model_path=os.environ.get("MODEL_DIR") + 'wiki-news-300d-1M.vec',
                action=Action.SUBSTITUTE),
            naw.GloVeAug(
                model_path=os.environ.get("MODEL_DIR") + 'glove.6B.50d.txt',
                action=Action.SUBSTITUTE)
        ]

def __init__(self, zone=(0.2, 0.8), coverage=1.,
                 color='white', noises=None, name='Noise_Aug', noise_factor=0.01, verbose=0):
        super().__init__(
            action=Action.SUBSTITUTE, name=name, device='cpu', verbose=verbose)

        if noise_factor != 0.01:
            print(WarningMessage.DEPRECATED.format('noise_factor', '0.0.12', ''))

        self.model = self.get_model(zone, coverage, color, noises)

    def __init__(self, model_path='.', action=Action.SUBSTITUTE,
                 name='WordEmbs_Aug', aug_min=1, aug_p=0.3, aug_n=5, n_gram_separator='_',
                 stopwords=None, tokenizer=None, reverse_tokenizer=None, verbose=0):
        super().__init__(
            action=action, name=name, aug_p=aug_p, aug_min=aug_min, stopwords=stopwords,
            tokenizer=tokenizer, reverse_tokenizer=reverse_tokenizer, verbose=verbose)
        self.model_path = model_path
        self.aug_n = aug_n
        self.model = self.get_model(force_reload=False)
        self.n_gram_separator = n_gram_separator

    def __init__(self, model_path='bert-base-uncased', tokenizer_path='bert-base-uncased', action=Action.SUBSTITUTE,
                 name='Bert_Aug', aug_min=1, aug_max=10, aug_p=0.3, aug_n=5, stopwords=None, device='cpu', verbose=0):
        super().__init__(
            action=action, name=name, aug_p=aug_p, aug_min=aug_min, aug_max=aug_max, tokenizer=None,
            stopwords=stopwords, verbose=verbose)
        self.model_path = model_path
        self.tokenizer_path = tokenizer_path
        self.aug_n = aug_n
        self.device = device
        self.model = self.get_model(device=device, force_reload=False)
        self.tokenizer = self.model.tokenizer.tokenize
        self.reverse_tokenizer = self._reverse_tokenizer

def __init__(self, mask_factor, name='TimeMasking_Aug', verbose=0):
        super(TimeMaskingAug, self).__init__(
            action=Action.SUBSTITUTE, name=name, device='cpu', verbose=verbose)

        self.model = self.get_model(mask_factor)

def __init__(self, name='OCR_Aug',  aug_char_min=1, aug_char_max=10, aug_char_p=0.3,
                 aug_word_p=0.3, aug_word_min=1, aug_word_max=10, stopwords=None,
                 tokenizer=None, reverse_tokenizer=None, verbose=0):
        super().__init__(
            action=Action.SUBSTITUTE, name=name, aug_char_min=aug_char_min, aug_char_max=aug_char_max,
            aug_char_p=aug_char_p, aug_word_min=aug_word_min, aug_word_max=aug_word_max, aug_word_p=aug_word_p,
            tokenizer=tokenizer, reverse_tokenizer=reverse_tokenizer, stopwords=stopwords, device='cpu',
            verbose=verbose)

        self.model = self.get_model()

def __init__(self, zone=(0.2, 0.8), coverage=1., duration=None,
                 factor=(0.5, 2),
                 speed_range=(0.5, 2), name='Speed_Aug', verbose=0):
        super().__init__(
            action=Action.SUBSTITUTE, name=name, device='cpu', verbose=verbose)

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

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