class_docstr: Computes the Smith-Waterman measure between two strings.
  The Smith-Waterman algorithm performs local sequence alignment;
  that is, for determining similar regions between two strings.
  Instead of looking at the total sequence, the Smith-Waterman algorithm compares
  segments of all possible lengths and optimizes the similarity measure.
  
  https://en.wikipedia.org/wiki/Smith%E2%80%93Waterman_algorithm
  https://github.com/Yomguithereal/talisman/blob/master/src/metrics/smith-waterman.js
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code: def maximum(self, *sequences: Sequence[object]) -> int:
      return min(map(len, sequences))
  
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 2 for calling the SmithWaterman obj's underlying member method code with example usage: obj.maximum() and returns: min