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: Passes the text through the tokenizer of the language model.
  
  Args:
      text: Text to be tokenized.
  
  Returns: List of token ids and token strings.
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code: def _lm_tokenize(self, text: Text) -> Tuple[List[int], List[Text]]:
      """Passes the text through the tokenizer of the language model.
  
      Args:
          text: Text to be tokenized.
  
      Returns: List of token ids and token strings.
      """
      split_token_ids = self.tokenizer.encode(
          text, add_special_tokens=False
      )
  
      split_token_strings = self.tokenizer.convert_ids_to_tokens(
          split_token_ids
      )
  
      return split_token_ids, split_token_strings
  
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 9 for calling the LanguageModelFeaturizer obj's underlying member method code with example usage: obj._lm_tokenize(text) and returns: split_token_ids,  split_token_strings