class_docstr: Intent classifier which uses the `mitie` library.
class_docstr_tok: ["Intent","classifier","which","uses","the","`","mitie","`","library","."]
docstr: Persists trained model (see parent class for full docstring).
docstr_tok: ["Persists","trained","model","(","see","parent","class","for","full","docstring",")","."]
code: def _persist(
      self, text_categorizer: "mitie.text_categorizer"
  ) -> None:
      """Persists trained model (see parent class for full docstring)."""
      with self._model_storage.write_to(self._resource) as directory:
          classifier_file = directory / "model.dat"
          text_categorizer.save_to_disk(
              str(classifier_file), pure_model=True
          )
  
imports: from __future__ import annotations
  import logging
  from rasa.nlu.featurizers.featurizer import Featurizer
  import typing
  from typing import Any, Dict, List, Optional, Text, Type
  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.classifiers.classifier import IntentClassifier
  from rasa.nlu.utils.mitie_utils import MitieModel, MitieNLP
  from rasa.nlu.constants import TOKENS_NAMES
  from rasa.shared.nlu.constants import TEXT, INTENT
  from rasa.shared.nlu.training_data.training_data import TrainingData
  from rasa.shared.nlu.training_data.message import Message
label_desc: Use image node_id 10 for calling the MitieIntentClassifier obj's underlying member method code with example usage: obj._persist(text_categorizer) without return types