input: 
  def find_matches(query, choices, threshold):
      """A fuzzy search algorithm that matches strings with near similarities.
      >>> find_matches('apple', ['apples', 'banana', 'pineapple'], 2)
      ['apples']
      >>> find_matches('apple', ['apples', 'aple', 'appl'], 1)
      ['aple', 'appl']
      """
      # Convert query and choices to lower case
      query, choices = query.lower(), [choice.lower() for choice in choices]
      # Initialize empty list to store matches
      matches = []
      
      # Loop through each choice
      for choice in choices:
          # Calculate Levenshtein distance between query and choice
          distance = levenshtein_distance(query, choice)
          # If distance is less than threshold, append choice to matches
          if distance <= threshold:
              matches.append(choice)
      
      return matches
  
output:     # Function to calculate Levenshtein distance
      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]