class_docstr: NLTK's sentence tokenizer (currently PunktSentenceTokenizer).
  Uses an unsupervised algorithm to build a model for abbreviation words,
  collocations, and words that start sentences,
  then uses that to find sentence boundaries.
class_docstr_tok: ["NLTK","'s","sentence","tokenizer","(","currently","PunktSentenceTokenizer",")",".","Uses","an","unsupervised","algorithm","to","build","a","model","for","abbreviation","words",",","collocations",",","and","words","that","start","sentences",",","then","uses","that","to","find","sentence","boundaries","."]
docstr: Return a list of sentences.
docstr_tok: ["Return","a","list","of","sentences","."]
code: def tokenize(self, text):
      """Return a list of sentences."""
      return nltk.tokenize.sent_tokenize(text)
  
file_path: TextBlob/textblob/tokenizers.py
imports: from __future__ import absolute_import
  from itertools import chain
  import nltk
  from textblob.utils import strip_punc
  from textblob.base import BaseTokenizer
  from textblob.decorators import requires_nltk_corpus
label_desc: Use image node_id 1 for calling the SentenceTokenizer obj's underlying member method code with example usage: obj.tokenize(text) and returns: nltk