class_docstr: Featurizer using ConveRT model.
  
  Loads the ConveRT(https://github.com/PolyAI-LDN/polyai-models#convert)
  model from TFHub and computes sentence and sequence level feature representations
  for dense featurizable attributes of each message object.
class_docstr_tok: ["Featurizer","using","ConveRT","model",".","Loads","the","ConveRT","(","https",":","\/\/github.com\/PolyAI-LDN\/polyai-models","#","convert",")","model","from","TFHub","and","computes","sentence","and","sequence","level","feature","representations","for","dense","featurizable","attributes","of","each","message","object","."]
docstr: Get the sequence and sentence features.
docstr_tok: ["Get","the","sequence","and","sentence","features","."]
code: def _get_features(
      sentence_encodings: np.ndarray,
      sequence_encodings: np.ndarray,
      number_of_tokens_in_sentence: List[int],
  ) -> Tuple[np.ndarray, np.ndarray]:
      """Get the sequence and sentence features."""
      sentence_embeddings = []
      sequence_embeddings = []
  
      for index in range(len(number_of_tokens_in_sentence)):
          sequence_length = number_of_tokens_in_sentence[index]
          sequence_encoding = sequence_encodings[index][
              :sequence_length
          ]
          sentence_encoding = sentence_encodings[index]
  
          sequence_embeddings.append(sequence_encoding)
          sentence_embeddings.append(sentence_encoding)
  
      return np.array(sequence_embeddings), np.array(
          sentence_embeddings
      )
  
imports: from __future__ import annotations
  import logging
  import os
  from typing import Any, Dict, List, Optional, Text, Tuple, Type
  import tensorflow
  from tensorflow.python.eager.wrap_function import WrappedFunction
  from tqdm import tqdm
  import numpy
  from rasa.engine.graph import GraphComponent, ExecutionContext
  from rasa.engine.recipes.default_recipe import DefaultV1Recipe
  from rasa.engine.storage.storage import ModelStorage
  from rasa.engine.storage.resource import Resource
  import rasa.shared.utils.io
  import rasa.core.utils
  from rasa.nlu.tokenizers.tokenizer import Token, Tokenizer
  from rasa.nlu.featurizers.dense_featurizer.dense_featurizer import DenseFeaturizer
  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, TOKENS_NAMES, NUMBER_OF_SUB_TOKENS
  from rasa.shared.nlu.constants import TEXT, ACTION_TEXT
  from rasa.exceptions import RasaException
  import rasa.nlu.utils
  import rasa.utils.train_utils
label_desc: Use image node_id 14 for calling the ConveRTFeaturizer obj's underlying member method code with example usage: obj._get_features(sentence_encodings, sequence_encodings, number_of_tokens_in_sentence) and returns: np,  np