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","`"]
code: def _parse_opts(self):
      self.tm_path = self.opts.tm_path
      self.fuzzy_corpus_ratio = self.opts.fuzzy_corpus_ratio
      self.fuzzy_threshold = self.opts.fuzzy_threshold
      self.tm_delimiter = self.opts.tm_delimiter
      self.fuzzy_token = self.opts.fuzzy_token
      self.fuzzymatch_min_length = self.opts.fuzzymatch_min_length
      self.fuzzymatch_max_length = self.opts.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 3 for calling the FuzzyMatchTransform obj's underlying member method code with example usage: obj._parse_opts() without return types