class_docstr: AggregateTokenizer, allowing one to combine multiple regular monolongual tokenizers into one tokenizer.
  The intuition is that we can use existing tokenizers "as is", without retraining, and associate each tokenizer with a language id
  during text processing (language id will be used to route the incoming text sample to the right tokenizer)
  as well as a token id range for detokenization (e.g. [0..127] for tokenizer A, [128..255] for tokenizer B) so
  that the orignal text could be reconstructed. Note that we assume that the incoming dict of langs / tokenizers
  is ordered, e.g. the first tokenizer will be assigned a lower interval of token ids
      Args:
      tokenizers: dict of tokenizers, keys are lang ids, values are actual tokenizers
code: def ids_to_words_and_langs(self, ids):
      words_and_langs = []
  
      word_ids = []  # tokens belonging to the current word
      for id in ids:
          offset_id = self.offset_token_ids_by_token_id[id]
          tokenizer = self.tokenizers_by_token_id[id]
          token = tokenizer.ids_to_tokens([offset_id])[0]
          if token.startswith("▁"):
              if len(word_ids) > 0:  # if this isn't the first word
                  word = self.ids_to_text(word_ids)
                  word = word.strip()  # strip for display purposes
                  lang = self.ids_to_lang(word_ids)
                  wl = {"word": word, "lang": lang}
                  words_and_langs.append(wl)
              word_ids = []
          word_ids.append(id)
  
      if len(word_ids) > 0:  # the last tokens
          word = self.ids_to_text(word_ids)
          word = word.strip()  # strip for display purposes
          lang = self.ids_to_lang(word_ids)
          wl = {"word": word, "lang": lang}
          words_and_langs.append(wl)
  
      return words_and_langs
  
imports: from typing import Dict, List, Union
  import numpy
  from nemo.collections.common.tokenizers.tokenizer_spec import TokenizerSpec
  from nemo.utils import logging
label_desc: Use image node_id 10 for calling the AggregateTokenizer obj's underlying member method code with example usage: obj.ids_to_words_and_langs(ids) and returns: words_and_langs