class_docstr: Intent classifier using the sklearn framework.
class_docstr_tok: ["Intent","classifier","using","the","sklearn","framework","."]
code: def _num_cv_splits(self, y: np.ndarray) -> int:
      folds = self.component_config["max_cross_validation_folds"]
      return max(2, min(folds, np.min(np.bincount(y)) // 5))
  
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 10 for calling the SklearnIntentClassifier obj's underlying member method code with example usage: obj._num_cv_splits(y) and returns: max