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.
class_docstr_tok: ["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","."]
docstr: Returns the extra python dependencies required.
docstr_tok: ["Returns","the","extra","python","dependencies","required","."]
code: def required_packages() -> List[Text]:
      """Returns the extra python dependencies required."""
      return ["transformers"]
  
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 6 for calling the LanguageModelFeaturizer obj's underlying member method code with example usage: obj.required_packages() and returns: list