input: def find_matches(query: str, choices: List[str], threshold: int) -> List[str]:
      """
      This function takes a query string, a list of choices, and a threshold for similarity.
      It returns a list of strings from choices that are similar to the query based on the Levenshtein distance.
  
      >>> find_matches('apple', ['apple', 'apples', 'banana', 'pineapple'], 1)
      ['apple', 'apples']
      >>> find_matches('bike', ['bike', 'bake', 'biker', 'bike shop'], 2)
      ['bike', 'biker']
      """
output:     from Levenshtein import distance
      return [choice for choice in choices if distance(query, choice) <= threshold]