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: Adds the precomputed word vectors to the messages features.
docstr_tok: ["Adds","the","precomputed","word","vectors","to","the","messages","features","."]
code: def _set_lm_features(
      self,
      doc: Dict[Text, Any],
      message: Message,
      attribute: Text = TEXT,
  ) -> None:
      """Adds the precomputed word vectors to the messages features."""
      sequence_features = doc[SEQUENCE_FEATURES]
      sentence_features = doc[SENTENCE_FEATURES]
  
      self.add_features_to_message(
          sequence=sequence_features,
          sentence=sentence_features,
          attribute=attribute,
          message=message,
      )
  
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 27 for calling the LanguageModelFeaturizer obj's underlying member method code with example usage: obj._set_lm_features(doc, message, attribute) without return types