class_docstr: A featurizer that uses transformer-based language models.
  
  This component loads a pre-trained language model
  from the Transformers library (https://github.com/huggingface/transformers)
  including BERT, GPT, GPT-2, xlnet, distilbert, and roberta.
  It also tokenizes and featurizes the featurizable dense attributes of
  each message.
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docstr: Adds the language and model-specific tokens used during training.
  
  Args:
      token_ids: List of token ids for each example in the batch.
  
  Returns: Augmented list of token ids for each example in the batch.
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code: def _add_lm_specific_special_tokens(
      self, token_ids: List[List[int]]
  ) -> List[List[int]]:
      """Adds the language and model-specific tokens used during training.
  
      Args:
          token_ids: List of token ids for each example in the batch.
  
      Returns: Augmented list of token ids for each example in the batch.
      """
      from rasa.nlu.utils.hugging_face.registry import (
          model_special_tokens_pre_processors,
      )
  
      augmented_tokens = [
          model_special_tokens_pre_processors[self.model_name](
              example_token_ids
          )
          for example_token_ids in token_ids
      ]
      return augmented_tokens
  
imports: from __future__ import annotations
  import numpy
  import logging
  from typing import Any, Text, List, Dict, Tuple, Type
  import tensorflow
  from rasa.engine.graph import ExecutionContext, GraphComponent
  from rasa.engine.recipes.default_recipe import DefaultV1Recipe
  from rasa.engine.storage.resource import Resource
  from rasa.engine.storage.storage import ModelStorage
  from rasa.nlu.featurizers.dense_featurizer.dense_featurizer import DenseFeaturizer
  from rasa.nlu.tokenizers.tokenizer import Token, Tokenizer
  from rasa.shared.nlu.training_data.training_data import TrainingData
  from rasa.shared.nlu.training_data.message import Message
  from rasa.nlu.constants import DENSE_FEATURIZABLE_ATTRIBUTES, SEQUENCE_FEATURES, SENTENCE_FEATURES, NO_LENGTH_RESTRICTION, NUMBER_OF_SUB_TOKENS, TOKENS_NAMES
  from rasa.shared.nlu.constants import TEXT, ACTION_TEXT
  from rasa.utils import train_utils
  from rasa.utils.tensorflow.model_data import ragged_array_to_ndarray
label_desc: Use image node_id 10 for calling the LanguageModelFeaturizer obj's underlying member method code with example usage: obj._add_lm_specific_special_tokens(token_ids) and returns: augmented_tokens