class_docstr: Perform fuzzy matching against a translation memory and
  augment source examples with target matches for Neural Fuzzy Repair.
  :cite:`bulte-tezcan-2019-neural`
class_docstr_tok: ["Perform","fuzzy","matching","against","a","translation","memory","and","augment","source","examples","with","target","matches","for","Neural","Fuzzy","Repair",".",":","cite",":","`","bulte-tezcan-2019-neural","`"]
docstr: Create the fuzzy matcher.
docstr_tok: ["Create","the","fuzzy","matcher","."]
code: def warm_up(self, vocabs=None):
      """Create the fuzzy matcher."""
  
      super().warm_up(None)
      self.matcher = FuzzyMatcher(
          self.tm_path,
          self.fuzzy_corpus_ratio,
          self.fuzzy_threshold,
          self.tm_delimiter,
          self.fuzzy_token,
          self.fuzzymatch_min_length,
          self.fuzzymatch_max_length,
      )
  
imports: from onmt.utils.logging import logger
  from onmt.transforms import register_transform
  from .transform import Transform
  from rapidfuzz import fuzz, process
  import numpy
  import time
label_desc: Use image node_id 5 for calling the FuzzyMatchTransform obj's underlying member method code with example usage: obj.warm_up(vocabs) without return types