class_docstr: Intent classifier using the sklearn framework.
class_docstr_tok: ["Intent","classifier","using","the","sklearn","framework","."]
docstr: Creates a new untrained component (see parent class for full docstring).
docstr_tok: ["Creates","a","new","untrained","component","(","see","parent","class","for","full","docstring",")","."]
code: def create(
      cls,
      config: Dict[Text, Any],
      model_storage: ModelStorage,
      resource: Resource,
      execution_context: ExecutionContext,
  ) -> SklearnIntentClassifier:
      """Creates a new untrained component (see parent class for full docstring)."""
      return cls(config, model_storage, resource)
  
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 4 for calling the SklearnIntentClassifier obj's underlying member method code with example usage: obj.create(cls, config, model_storage, resource, execution_context) and returns: cls