class_docstr: Computes the Jaro-Winkler measure between two strings.
  The Jaro-Winkler measure is designed to capture cases where two strings
  have a low Jaro score, but share a prefix.
  and thus are likely to match.
  
  https://en.wikipedia.org/wiki/Jaro%E2%80%93Winkler_distance
  https://github.com/Yomguithereal/talisman/blob/master/src/metrics/jaro.js
  https://github.com/Yomguithereal/talisman/blob/master/src/metrics/jaro-winkler.js
class_docstr_tok: ["Computes","the","Jaro-Winkler","measure","between","two","strings",".","The","Jaro-Winkler","measure","is","designed","to","capture","cases","where","two","strings","have","a","low","Jaro","score",",","but","share","a","prefix",".","and","thus","are","likely","to","match",".","https",":","\/\/en.wikipedia.org\/wiki\/Jaro","%","E2","%","80","%","93Winkler_distance","https",":","\/\/github.com\/Yomguithereal\/talisman\/blob\/master\/src\/metrics\/jaro.js","https",":","\/\/github.com\/Yomguithereal\/talisman\/blob\/master\/src\/metrics\/jaro-winkler.js"]
code: def __init__(
      self,
      long_tolerance: bool = False,
      winklerize: bool = True,
      qval: int = 1,
      external: bool = True,
  ) -> None:
      self.qval = qval
      self.long_tolerance = long_tolerance
      self.winklerize = winklerize
      self.external = external
  
imports: from __future__ import annotations
  from collections import defaultdict
  from itertools import zip_longest
  from typing import Any, Sequence, TypeVar
  from .base import Base, BaseSimilarity
  from .types import SimFunc, TestFunc
label_desc: Use image node_id 1 to create a new JaroWinkler object from inherited base classes: _BaseSimilarity with example: obj = JaroWinkler(long_tolerance, winklerize, qval, external)