code: def minimize(
      self,
      trie,
      dict_storage=False,
      make_cashed=False,
      make_numpied=False,
      precompute_symbols=None,
      allow_spaces=False,
      return_groups=False,
  ):
      N = len(trie)
      if N == 0:
          raise ValueError("Trie should be non-empty")
      node_classes = np.full(shape=(N,), fill_value=-1, dtype=int)
      order = self.generate_postorder(trie)
      # processing the first node
      index = order[0]
      node_classes[index] = 0
      class_representatives = [index]
      node_key = ((), (), trie.is_final(index))
      classes, class_keys = {node_key: 0}, [node_key]
      curr_index = 1
      for index in order[1:]:
          letter_indexes = tuple(
              trie._get_letters(index, return_indexes=True)
          )
          children = trie._get_children(index)
          children_classes = tuple(node_classes[i] for i in children)
          key = (letter_indexes, children_classes, trie.is_final(index))
          key_class = classes.get(key, None)
          if key_class is not None:
              node_classes[index] = key_class
          else:
              # появился новый класс
              class_keys.append(key)
              classes[key] = node_classes[index] = curr_index
              class_representatives.append(curr_index)
              curr_index += 1
      # построение нового дерева
      compressed = Trie(
          trie.alphabet,
          is_numpied=make_numpied,
          dict_storage=dict_storage,
          allow_spaces=allow_spaces,
          precompute_symbols=precompute_symbols,
      )
      L = len(classes)
      new_final = [elem[2] for elem in class_keys[::-1]]
      if dict_storage:
          new_graph = [defaultdict(int) for _ in range(L)]
      elif make_numpied:
          new_graph = np.full(
              shape=(L, len(trie.alphabet)),
              fill_value=Trie.NO_NODE,
              dtype=int,
          )
          new_final = np.array(new_final, dtype=bool)
      else:
          new_graph = [
              [Trie.NO_NODE for a in trie.alphabet] for i in range(L)
          ]
      for (indexes, children, final), class_index in sorted(
          classes.items(), key=(lambda x: x[1])
      ):
          row = new_graph[L - class_index - 1]
          for i, child_index in zip(indexes, children):
              row[i] = L - child_index - 1
      compressed.graph = new_graph
      compressed.root = L - node_classes[trie.root] - 1
      compressed.final = new_final
      compressed.nodes_number = L
      compressed.data = [None] * L
      if make_cashed:
          compressed.make_cashed()
      if precompute_symbols is not None:
          if (
              trie.is_terminated
              and trie.precompute_symbols
              and trie.allow_spaces == allow_spaces
          ):
              # копируем будущие символы из исходного дерева
              # нужно, чтобы возврат из финальных состояний в начальное был одинаковым в обоих деревьях
              for i, node_index in enumerate(
                  class_representatives[::-1]
              ):
                  # будущие символы для представителя i-го класса
                  compressed.data[i] = copy.copy(trie.data[node_index])
          else:
              precompute_future_symbols(
                  compressed, precompute_symbols, allow_spaces
              )
      if return_groups:
          node_classes = [L - i - 1 for i in node_classes]
          return compressed, node_classes
      else:
          return compressed
  
imports: import copy
  from collections import defaultdict
  import numpy
label_desc: Use image node_id 2 for calling the TrieMinimizer obj's underlying member method code with example usage: obj.minimize(trie, dict_storage, make_cashed, make_numpied, precompute_symbols, allow_spaces, return_groups) and returns: compressed,  node_classes, compressed