class_docstr: Provides data for the featurization pipeline.
  
  During training as well as during inference, the converter de-duplicates the given
  data (i.e. story graph or list of messages) such that each text and intent from a
  user message and each action name and action text appears exactly once.
class_docstr_tok: ["Provides","data","for","the","featurization","pipeline",".","During","training","as","well","as","during","inference",",","the","converter","de-duplicates","the","given","data","(","i.e",".","story","graph","or","list","of","messages",")","such","that","each","text","and","intent","from","a","user","message","and","each","action","name","and","action","text","appears","exactly","once","."]
docstr: Creates a new instance (see parent class for full docstring).
docstr_tok: ["Creates","a","new","instance","(","see","parent","class","for","full","docstring",")","."]
code: def create(
      cls,
      config: Dict[Text, Any],
      model_storage: ModelStorage,
      resource: Resource,
      execution_context: ExecutionContext,
  ) -> CoreFeaturizationInputConverter:
      """Creates a new instance (see parent class for full docstring)."""
      return cls()
  
imports: from __future__ import annotations
  from typing import Optional, Text, Dict, List, Union, Iterable, Any
  from collections.abc import ValuesView, KeysView
  from rasa.engine.graph import GraphComponent
  from rasa.engine.storage.storage import ModelStorage
  from rasa.engine.storage.resource import Resource
  from rasa.engine.graph import ExecutionContext
  from rasa.shared.core.domain import Domain, SubState
  from rasa.shared.core.events import ActionExecuted, UserUttered, Event
  from rasa.shared.core.trackers import DialogueStateTracker
  from rasa.shared.core.training_data.structures import StoryGraph
  from rasa.shared.nlu.constants import ACTION_NAME, ACTION_TEXT, INTENT, TEXT
  from rasa.shared.nlu.training_data.message import Message
  from rasa.shared.nlu.training_data.training_data import TrainingData
  from rasa.shared.nlu.training_data.features import Features
  import rasa.shared.utils.io
label_desc: Use image node_id 1 for calling the CoreFeaturizationInputConverter obj's underlying member method code with example usage: obj.create(cls, config, model_storage, resource, execution_context) and returns: cls