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: Initializes a `ConveRTFeaturizer`.
  
  Args:
      name: An identifier for this featurizer.
      config: The configuration.
docstr_tok: ["Initializes","a","`","ConveRTFeaturizer","`",".","Args",":","name",":","An","identifier","for","this","featurizer",".","config",":","The","configuration","."]
code: def __init__(self, name: Text, config: Dict[Text, Any]) -> None:
      """Initializes a `ConveRTFeaturizer`.
  
      Args:
          name: An identifier for this featurizer.
          config: The configuration.
      """
      super().__init__(name=name, config=config)
  
      model_url = self._config["model_url"]
      self.model_url = (
          model_url
          if rasa.nlu.utils.is_url(model_url)
          else os.path.abspath(model_url)
      )
  
      self.module = train_utils.load_tf_hub_model(self.model_url)
  
      self.tokenize_signature: WrappedFunction = self._get_signature(
          "tokenize", self.module
      )
      self.sequence_encoding_signature: WrappedFunction = (
          self._get_signature("encode_sequence", self.module)
      )
      self.sentence_encoding_signature: WrappedFunction = (
          self._get_signature("default", self.module)
      )
  
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 6 to create a new ConveRTFeaturizer object from inherited base classes: DenseFeaturizer, GraphComponent with example: obj = ConveRTFeaturizer(name, config)