How to use the medcat.utils.spacy_pipe.SpacyPipe function in medcat

def __init__(self, vocab=None, pretrained_cdb=None, word_tokenizer=None):
        self.vocab = vocab
        if pretrained_cdb is None:
            self.cdb = CDB()
        else:
            self.cdb = pretrained_cdb

        # Build the required spacy pipeline
        self.nlp = SpacyPipe(spacy_split_all, disable=['ner', 'parser'])
        self.nlp.add_punct_tagger(tagger=partial(spacy_tag_punct, skip_stopwords=self.SKIP_STOPWORDS))
        # Get the tokenizer
        if word_tokenizer is not None:
            self.tokenizer = word_tokenizer
        else:
            self.tokenizer = self._tok

def __init__(self, cdb, vocab=None, word_tokenizer=None):
        self.cdb = cdb

        self.w2v = None
        if vocab is not None:
            self.vocab = vocab
        else:
            self.vocab = Vocab()

        # Build the required spacy pipeline
        self.nlp = SpacyPipe(spacy_split_all, disable=['ner', 'parser', 'vectors', 'textcat'])

        # Get the tokenizer
        if word_tokenizer is not None:
            self.tokenizer = word_tokenizer
        else:
            self.tokenizer = self._tok

def __init__(self, cdb, vocab=None, skip_stopwords=True, meta_cats=[]):
        self.cdb = cdb
        self.vocab = vocab
        # Build the required spacy pipeline
        self.nlp = SpacyPipe(spacy_split_all)
        #self.nlp.add_punct_tagger(tagger=spacy_tag_punct)
        self.nlp.add_punct_tagger(tagger=partial(spacy_tag_punct,
                                                 skip_stopwords=skip_stopwords,
                                                 keep_punct=self.KEEP_PUNCT))

        # Add spell checker pipe
        self.spell_checker = CustomSpellChecker(cdb_vocab=self.cdb.vocab, data_vocab=self.vocab)
        self.nlp.add_spell_checker(spell_checker=self.spell_checker)

        # Add cat
        self.spacy_cat = SpacyCat(cdb=self.cdb, vocab=self.vocab)
        self.nlp.add_cat(spacy_cat=self.spacy_cat)

        for meta_cat in meta_cats:
            self.nlp.add_meta_cat(meta_cat, meta_cat.category_name)

def __init__(self, cdb, vocab=None, skip_stopwords=True, meta_cats=[], config={}, tokenizer=None):
        self.cdb = cdb
        self.vocab = vocab
        self.config = config

        # Build the spacy pipeline
        self.nlp = SpacyPipe(spacy_split_all)

        #self.nlp.add_punct_tagger(tagger=spacy_tag_punct)
        self.nlp.add_punct_tagger(tagger=partial(spacy_tag_punct,
                                                 skip_stopwords=skip_stopwords,
                                                 keep_punct=self.config.get("keep_punct", [':', '.'])))

        # Add spell checker
        self.spell_checker = CustomSpellChecker(cdb_vocab=self.cdb.vocab, data_vocab=self.vocab)
        self.nlp.add_spell_checker(spell_checker=self.spell_checker)

        # Add them cat class that does entity detection
        self.spacy_cat = SpacyCat(cdb=self.cdb, vocab=self.vocab, tokenizer=tokenizer)
        self.nlp.add_cat(spacy_cat=self.spacy_cat)

        # Add meta_annotaiton classes if they exist
        self._meta_annotations = False

def __init__(self, vocab=None, pretrained_cdb=None, tokenizer=None):
        self.vocab = vocab
        if pretrained_cdb is None:
            self.cdb = CDB()
        else:
            self.cdb = pretrained_cdb
        # Build the required spacy pipeline
        self.nlp = SpacyPipe(spacy_split_all, disable=['ner', 'parser'])
        self.nlp.add_punct_tagger(tagger=partial(spacy_tag_punct, skip_stopwords=False))
        # Get the tokenizer
        if tokenizer is not None:
            self.tokenizer = tokenizer
        else:
            self.tokenizer = self._tok #BertTokenizer.from_pretrained('bert-base-uncased')