class_docstr: Used by `Tokenizers` which split a single message into multiple `Token`s.
docstr: Create a `Token`.
  
  Args:
      text: The token text.
      start: The start index of the token within the entire message.
      end: The end index of the token within the entire message.
      data: Additional token data.
      lemma: An optional lemmatized version of the token text.
docstr_tok: ["Create","a","`","Token","`",".","Args",":","text",":","The","token","text",".","start",":","The","start","index","of","the","token","within","the","entire","message",".","end",":","The","end","index","of","the","token","within","the","entire","message",".","data",":","Additional","token","data",".","lemma",":","An","optional","lemmatized","version","of","the","token","text","."]
code: def __init__(
      self,
      text: Text,
      start: int,
      end: Optional[int] = None,
      data: Optional[Dict[Text, Any]] = None,
      lemma: Optional[Text] = None,
  ) -> None:
      """Create a `Token`.
  
      Args:
          text: The token text.
          start: The start index of the token within the entire message.
          end: The end index of the token within the entire message.
          data: Additional token data.
          lemma: An optional lemmatized version of the token text.
      """
      self.text = text
      self.start = start
      self.end = end if end else start + len(text)
  
      self.data = data if data else {}
      self.lemma = lemma or text
  
imports: import abc
  import logging
  import re
  from typing import Text, List, Dict, Any, Optional
  from rasa.engine.graph import ExecutionContext, GraphComponent
  from rasa.engine.storage.resource import Resource
  from rasa.engine.storage.storage import ModelStorage
  from rasa.shared.nlu.training_data.training_data import TrainingData
  from rasa.shared.nlu.training_data.message import Message
  from rasa.nlu.constants import TOKENS_NAMES, MESSAGE_ATTRIBUTES
  from rasa.shared.nlu.constants import INTENT, INTENT_RESPONSE_KEY, RESPONSE_IDENTIFIER_DELIMITER, ACTION_NAME
  import rasa.shared.utils.io
label_desc: Use image node_id 1 to create a new Token object  with example: obj = Token(text, start, end, data, lemma)