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 token_to_id(self, token, lang_id):
      tokenizer = self.tokenizers_dict[lang_id]
      return (
          tokenizer.token_to_id(token) + self.token_id_offset[lang_id]
      )
  
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 7 for calling the AggregateTokenizer obj's underlying member method code with example usage: obj.token_to_id(token, lang_id) and returns: unknown