class_docstr: Extracts entities via lookup tables and regexes defined in the training data.
docstr: Creates a new `GraphComponent`.
  
  Args:
      config: This config overrides the `default_config`.
      model_storage: Storage which graph components can use to persist and load
          themselves.
      resource: Resource locator for this component which can be used to persist
          and load itself from the `model_storage`.
      execution_context: Information about the current graph run. Unused.
  
  Returns: An instantiated `GraphComponent`.
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code: def create(
      cls,
      config: Dict[Text, Any],
      model_storage: ModelStorage,
      resource: Resource,
      execution_context: ExecutionContext,
  ) -> RegexEntityExtractor:
      """Creates a new `GraphComponent`.
  
      Args:
          config: This config overrides the `default_config`.
          model_storage: Storage which graph components can use to persist and load
              themselves.
          resource: Resource locator for this component which can be used to persist
              and load itself from the `model_storage`.
          execution_context: Information about the current graph run. Unused.
  
      Returns: An instantiated `GraphComponent`.
      """
      return cls(config, model_storage, resource)
  
imports: from __future__ import annotations
  import logging
  import re
  from typing import Any, Dict, List, Optional, Text
  from rasa.engine.graph import GraphComponent, ExecutionContext
  from rasa.engine.recipes.default_recipe import DefaultV1Recipe
  from rasa.engine.storage.storage import ModelStorage
  from rasa.engine.storage.resource import Resource
  import rasa.shared.utils.io
  import rasa.nlu.utils.pattern_utils
  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 ENTITIES, ENTITY_ATTRIBUTE_VALUE, ENTITY_ATTRIBUTE_START, ENTITY_ATTRIBUTE_END, TEXT, ENTITY_ATTRIBUTE_TYPE
  from rasa.nlu.extractors.extractor import EntityExtractorMixin
label_desc: Use image node_id 2 for calling the RegexEntityExtractor obj's underlying member method code with example usage: obj.create(cls, config, model_storage, resource, execution_context) and returns: cls