          DOCS: https://spacy.io/api/sentencerecognizer#initialize
          """
          validate_get_examples(get_examples, "SentenceRecognizer.initialize")
          doc_sample = []
          label_sample = []
          assert self.labels, Errors.E924.format(name=self.name)
          for example in islice(get_examples(), 10):
              doc_sample.append(example.x)
              gold_tags = example.get_aligned("SENT_START")
 
</s> remove         lexeme_norms = self.vocab.lookups.get_table("lexeme_norm", {})
        if len(lexeme_norms) == 0 and self.vocab.lang in util.LEXEME_NORM_LANGS:
            langs = ", ".join(util.LEXEME_NORM_LANGS)
            util.logger.debug(Warnings.W033.format(model="parser or NER", langs=langs))
 </s> add         util.check_lexeme_norms(self.vocab, "parser or NER") </s> remove LEXEME_NORM_LANGS = ["da", "de", "el", "en", "id", "lb", "pt", "ru", "sr", "ta", "th"]
 </s> add LEXEME_NORM_LANGS = ["cs", "da", "de", "el", "en", "id", "lb", "mk", "pt", "ru", "sr", "ta", "th"]
        util.check_lexeme_norms(self.vocab, "senter")
          DOCS: https://spacy.io/api/sentencerecognizer#initialize
          """
          validate_get_examples(get_examples, "SentenceRecognizer.initialize")
        util.check_lexeme_norms(self.vocab, "senter")
          doc_sample = []
          label_sample = []
          assert self.labels, Errors.E924.format(name=self.name)
          for example in islice(get_examples(), 10):
              doc_sample.append(example.x)
              gold_tags = example.get_aligned("SENT_START")
keep keep add keep keep keep keep keep keep
 Update lexeme_norm checks

* Add util method for check
* Add new languages to list with lexeme norm tables
* Add check to all relevant components
* Add config details to warning message

Note that we're not actually inspecting the model config to see if
`NORM` is used as an attribute, so it may warn in cases where it's not
relevant. 
spacy/pipeline/senter.pyx