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/.
docstr: kNN fuzzy search: find the `topn` most similar terms from `self.dictionary` to `t1`.
docstr_tok: ["kNN","fuzzy","search",":","find","the","`","topn","`","most","similar","terms","from","`","self.dictionary","`","to","`","t1","`","."]
code: def most_similar(self, t1, topn=10):
      """kNN fuzzy search: find the `topn` most similar terms from `self.dictionary` to `t1`."""
      result = (
          {}
      )  # map of {dictionary term => its levenshtein similarity to t1}
      if self.max_distance > 0:
          effective_topn = (
              topn + 1 if t1 in self.dictionary.token2id else topn
          )
          effective_topn = min(len(self.dictionary), effective_topn)
  
          # Implement a "distance backoff" algorithm:
          # Start with max_distance=1, for performance. And if that doesn't return enough results,
          # continue with max_distance=2 etc, all the way until self.max_distance which
          # is a hard cutoff.
          # At that point stop searching, even if we don't have topn results yet.
          #
          # We use the backoff algo to speed up queries for short terms. These return enough results already
          # with max_distance=1.
          #
          # See the discussion at https://github.com/RaRe-Technologies/gensim/pull/3146
          for distance in range(1, self.max_distance + 1):
              for t2 in self.index.query(t1, distance).get(
                  distance, []
              ):
                  if t1 == t2:
                      continue
                  similarity = self.levsim(t1, t2, distance)
                  if similarity > 0:
                      result[t2] = similarity
              if len(result) >= effective_topn:
                  break
  
      return sorted(result.items(), key=lambda x: (-x[1], x[0]))[:topn]
  
imports: import logging
  from gensim.similarities.termsim import TermSimilarityIndex
  from gensim import utils
label_desc: Use image node_id 3 for calling the LevenshteinSimilarityIndex obj's underlying member method code with example usage: obj.most_similar(t1, topn) and returns: sorted