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: Feeds the padded batch to the language model.
  
  Args:
      batch_attention_mask: Mask of 0s and 1s which indicate whether the token
      is a padding token or not.
      padded_token_ids: Batch of token ids for each example. The batch is padded
      and hence can be fed at once.
  
  Returns:
      Sequence level representations from the language model.
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code: def _compute_batch_sequence_features(
      self,
      batch_attention_mask: np.ndarray,
      padded_token_ids: List[List[int]],
  ) -> np.ndarray:
      """Feeds the padded batch to the language model.
  
      Args:
          batch_attention_mask: Mask of 0s and 1s which indicate whether the token
          is a padding token or not.
          padded_token_ids: Batch of token ids for each example. The batch is padded
          and hence can be fed at once.
  
      Returns:
          Sequence level representations from the language model.
      """
      model_outputs = self.model(
          tf.convert_to_tensor(padded_token_ids),
          attention_mask=tf.convert_to_tensor(batch_attention_mask),
      )
  
      # sequence hidden states is always the first output from all models
      sequence_hidden_states = model_outputs[0]
  
      sequence_hidden_states = sequence_hidden_states.numpy()
      return sequence_hidden_states
  
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 19 for calling the LanguageModelFeaturizer obj's underlying member method code with example usage: obj._compute_batch_sequence_features(batch_attention_mask, padded_token_ids) and returns: sequence_hidden_states