class_docstr: Retrieve the most similar terms from a static set of terms ("dictionary")
  given a query term, using Levenshtein similarity.
  
  "Levenshtein similarity" is a modification of the Levenshtein (edit) distance,
  defined in [charletetal17]_.
  
  This implementation uses the :class:`~gensim.similarities.fastss.FastSS` algorithm
  for fast kNN nearest-neighbor retrieval.
  
  Parameters
  ----------
  dictionary : :class:`~gensim.corpora.dictionary.Dictionary`
      A dictionary that specifies the considered terms.
  alpha : float, optional
      Multiplicative factor `alpha` for the Levenshtein similarity. See [charletetal17]_.
  beta : float, optional
      The exponential factor `beta` for the Levenshtein similarity. See [charletetal17]_.
  max_distance : int, optional
      Do not consider terms with Levenshtein distance larger than this as
      "similar". This is done for performance reasons: keep this value below 3
      for reasonable retrieval performance. Default is 1.
  
  See Also
  --------
  :class:`~gensim.similarities.termsim.WordEmbeddingSimilarityIndex`
      Retrieve most similar terms for a given term using the cosine
      similarity over word embeddings.
  :class:`~gensim.similarities.termsim.SparseTermSimilarityMatrix`
      Build a term similarity matrix and compute the Soft Cosine Measure.
  
  References
  ----------
  
  .. [charletetal17] Delphine Charlet and Geraldine Damnati, "SimBow at SemEval-2017 Task 3:
     Soft-Cosine Semantic Similarity between Questions for Community Question Answering", 2017,
     https://www.aclweb.org/anthology/S17-2051/.
code: def __init__(self, dictionary, alpha=1.8, beta=5.0, max_distance=2):
      self.dictionary = dictionary
      self.alpha = alpha
      self.beta = beta
      self.max_distance = max_distance
      logger.info("creating FastSS index from %s", dictionary)
      self.index = FastSS(
          words=self.dictionary.values(), max_dist=max_distance
      )
      super(LevenshteinSimilarityIndex, self).__init__()
  
imports: import logging
  from gensim.similarities.termsim import TermSimilarityIndex
  from gensim import utils
label_desc: Use image node_id 1 to create a new LevenshteinSimilarityIndex object from inherited base classes: TermSimilarityIndex with example: obj = LevenshteinSimilarityIndex(dictionary, alpha, beta, max_distance)