class_docstr: Base class for sentences tokenization
class_docstr_tok: ["Base","class","for","sentences","tokenization"]
docstr: Method for tokenizing sentences with pretrained punkt models; can
  be overridden by language-specific tokenizers.
  
  :rtype: list
  :param text: text to be tokenized into sentences
  :type text: str
  :param model: tokenizer object to used # Should be in init?
  :type model
docstr_tok: ["Method","for","tokenizing","sentences","with","pretrained","punkt","models",";","can","be","overridden","by","language-specific","tokenizers",".",":","rtype",":","list",":","param","text",":","text","to","be","tokenized","into","sentences",":","type","text",":","str",":","param","model",":","tokenizer","object","to","used","#","Should","be","in","init","?",":","type","model"]
code: def tokenize(self, text: str, model=None) -> List[str]:
      """Method for tokenizing sentences with pretrained punkt models; can
      be overridden by language-specific tokenizers.
  
      :rtype: list
      :param text: text to be tokenized into sentences
      :type text: str
      :param model: tokenizer object to used # Should be in init?
      :type model
      """
      if not hasattr(self, "model") or not self.model:
          self.model = model
  
      tokenizer = self.model
      if not hasattr(tokenizer, "tokenize"):
          raise CLTKException("model does not have 'tokenize' method.")
      if self.lang_vars:
          tokenizer._lang_vars = self.lang_vars
      return tokenizer.tokenize(text)
  
file_path: cltk/src/cltk/sentence/sentence.py
imports: import os
  import re
  from abc import ABC, abstractmethod
  from typing import List
  from nltk.tokenize.punkt import PunktSentenceTokenizer
  from cltk.core import CLTKException
  from cltk.utils import CLTK_DATA_DIR
  from cltk.utils.file_operations import open_pickle
label_desc: Use image node_id 2 for calling the SentenceTokenizer obj's underlying member method code with example usage: obj.tokenize(text, model) and returns: tokenizer