class_docstr: Intent classifier using the sklearn framework.
class_docstr_tok: ["Intent","classifier","using","the","sklearn","framework","."]
docstr: Transforms a list of strings into numeric label representation.
  
  :param labels: List of labels to convert to numeric representation
docstr_tok: ["Transforms","a","list","of","strings","into","numeric","label","representation",".",":","param","labels",":","List","of","labels","to","convert","to","numeric","representation"]
code: def transform_labels_str2num(self, labels: List[Text]) -> np.ndarray:
      """Transforms a list of strings into numeric label representation.
  
      :param labels: List of labels to convert to numeric representation
      """
      return self.le.fit_transform(labels)
  
imports: from __future__ import annotations
  import logging
  from rasa.nlu.featurizers.dense_featurizer.dense_featurizer import DenseFeaturizer
  import typing
  import warnings
  from typing import Any, Dict, List, Optional, Text, Tuple, Type
  import numpy
  import rasa.shared.utils.io
  import rasa.utils.io
  from rasa.engine.graph import GraphComponent, ExecutionContext
  from rasa.engine.recipes.default_recipe import DefaultV1Recipe
  from rasa.engine.storage.resource import Resource
  from rasa.engine.storage.storage import ModelStorage
  from rasa.shared.constants import DOCS_URL_TRAINING_DATA_NLU
  from rasa.nlu.classifiers import LABEL_RANKING_LENGTH
  from rasa.shared.exceptions import RasaException
  from rasa.shared.nlu.constants import TEXT
  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.utils.tensorflow.constants import FEATURIZERS
label_desc: Use image node_id 6 for calling the SklearnIntentClassifier obj's underlying member method code with example usage: obj.transform_labels_str2num(labels) and returns: self