class_docstr: Intent classifier which uses a Logistic Regression underneath
class_docstr_tok: ["Intent","classifier","which","uses","a","Logistic","Regression","underneath"]
docstr: Persists the object at the given path
docstr_tok: ["Persists","the","object","at","the","given","path"]
code: def persist(self, path):
      """Persists the object at the given path"""
      path.mkdir()
  
      featurizer = None
      if self.featurizer is not None:
          featurizer = "featurizer"
          featurizer_path = path / featurizer
          self.featurizer.persist(featurizer_path)
  
      coeffs = None
      intercept = None
      t_ = None
      if self.classifier is not None:
          coeffs = self.classifier.coef_.tolist()
          intercept = self.classifier.intercept_.tolist()
          t_ = self.classifier.t_
  
      self_as_dict = {
          "config": self.config.to_dict(),
          "coeffs": coeffs,
          "intercept": intercept,
          "t_": t_,
          "intent_list": self.intent_list,
          "featurizer": featurizer,
      }
  
      classifier_json = json_string(self_as_dict)
      with (path / "intent_classifier.json").open(
          mode="w", encoding="utf8"
      ) as f:
          f.write(classifier_json)
      self.persist_metadata(path)
  
imports: from __future__ import unicode_literals
  import json
  import logging
  from builtins import range, str, zip
  from pathlib import Path
  from snips_nlu.common.log_utils import DifferedLoggingMessage, log_elapsed_time
  from snips_nlu.common.utils import check_persisted_path, fitted_required, json_string
  from snips_nlu.constants import LANGUAGE, RES_PROBA
  from snips_nlu.dataset import validate_and_format_dataset
  from snips_nlu.exceptions import LoadingError, _EmptyDatasetUtterancesError
  from snips_nlu.intent_classifier.featurizer import Featurizer
  from snips_nlu.intent_classifier.intent_classifier import IntentClassifier
  from snips_nlu.intent_classifier.log_reg_classifier_utils import build_training_data, get_regularization_factor, text_to_utterance
  from snips_nlu.pipeline.configs import LogRegIntentClassifierConfig
  from snips_nlu.result import intent_classification_result
label_desc: Use image node_id 8 for calling the LogRegIntentClassifier obj's underlying member method code with example usage: obj.persist(path) without return types