class_docstr: Intent classifier using the Logistic Regression.
docstr: Train the intent classifier on a data set.
docstr_tok: ["Train","the","intent","classifier","on","a","data","set","."]
code: def train(self, training_data: TrainingData) -> Resource:
      """Train the intent classifier on a data set."""
      X, y = self._create_training_matrix(training_data)
      if X.shape[0] == 0:
          logger.debug(
              f"Cannot train '{self.__class__.__name__}'. No data was provided. "
              f"Skipping training of the classifier."
          )
          return self._resource
  
      self.clf.fit(X, y)
      self.persist()
  
      return self._resource
  
imports: import logging
  from typing import Any, Text, Dict, List, Type, Tuple
  import joblib
  from scipy.sparse import hstack, vstack, csr_matrix
  from sklearn.linear_model import LogisticRegression
  from rasa.engine.storage.resource import Resource
  from rasa.engine.storage.storage import ModelStorage
  from rasa.engine.recipes.default_recipe import DefaultV1Recipe
  from rasa.engine.graph import ExecutionContext, GraphComponent
  from rasa.nlu.classifiers import LABEL_RANKING_LENGTH
  from rasa.nlu.featurizers.featurizer import Featurizer
  from rasa.nlu.classifiers.classifier import IntentClassifier
  from rasa.shared.nlu.training_data.training_data import TrainingData
  from rasa.shared.nlu.training_data.message import Message
  from rasa.shared.nlu.constants import TEXT, INTENT
  from rasa.utils.tensorflow.constants import RANKING_LENGTH
label_desc: Use image node_id 7 for calling the LogisticRegressionClassifier obj's underlying member method code with example usage: obj.train(training_data) and returns: self, self