input: Write a function to implement a fuzzy search algorithm that matches strings with near similarities. The function should compute Levenshtein distance between a query and a list of choices, and return the choices that fall within a specified distance threshold.
  assert find_matches("apple", ["apples", "banana", "pineapple"], 3) == ["apples", "pineapple"]
  assert find_matches("cat", ["bat", "dog", "car"], 2) == ["bat", "car"]
  assert find_matches("grape", ["grapes", "grain", "grace"], 1) == ["grapes"]
  assert find_matches("elephant", ["elephan", "family", "elphant"], 2) == ["elephan", "elphant"]
  assert find_matches("hello", ["world", "help", "jello"], 4) == ["help", "jello"]
output: def find_matches(query, choices, threshold):
      def levenshtein_distance(s1, s2):
          if len(s1) < len(s2):
              return levenshtein_distance(s2, s1)
          if len(s2) == 0:
              return len(s1)
          previous_row = range(len(s2) + 1)
          for i, c1 in enumerate(s1):
              current_row = [i + 1]
              for j, c2 in enumerate(s2):
                  insertions = previous_row[j + 1] + 1
                  deletions = current_row[j] + 1
                  substitutions = previous_row[j] + (c1 != c2)
                  current_row.append(min(insertions, deletions, substitutions))
              previous_row = current_row
          return previous_row[-1]
  
      return [choice for choice in choices if levenshtein_distance(query, choice) <= threshold]