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: Computes token ids and token strings for each example in batch.
  
  A token id is the id of that token in the vocabulary of the language model.
  
  Args:
      batch_examples: Batch of message objects for which tokens need to be
      computed.
      attribute: Property of message to be processed, one of ``TEXT`` or
      ``RESPONSE``.
  
  Returns: List of token strings and token ids for each example in the batch.
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code: def _get_token_ids_for_batch(
      self, batch_examples: List[Message], attribute: Text
  ) -> Tuple[List[List[Token]], List[List[int]]]:
      """Computes token ids and token strings for each example in batch.
  
      A token id is the id of that token in the vocabulary of the language model.
  
      Args:
          batch_examples: Batch of message objects for which tokens need to be
          computed.
          attribute: Property of message to be processed, one of ``TEXT`` or
          ``RESPONSE``.
  
      Returns: List of token strings and token ids for each example in the batch.
      """
      batch_token_ids = []
      batch_tokens = []
      for example in batch_examples:
          example_tokens, example_token_ids = self._tokenize_example(
              example, attribute
          )
          batch_tokens.append(example_tokens)
          batch_token_ids.append(example_token_ids)
  
      return batch_tokens, batch_token_ids
  
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 14 for calling the LanguageModelFeaturizer obj's underlying member method code with example usage: obj._get_token_ids_for_batch(batch_examples, attribute) and returns: batch_tokens,  batch_token_ids