class_docstr: Interface to the Stanford Tokenizer
  
  >>> from nltk.tokenize.stanford import StanfordTokenizer
  >>> s = "Good muffins cost $3.88\nin New York.  Please buy me\ntwo of them.\nThanks."
  >>> StanfordTokenizer().tokenize(s) # doctest: +SKIP
  ['Good', 'muffins', 'cost', '$', '3.88', 'in', 'New', 'York', '.', 'Please', 'buy', 'me', 'two', 'of', 'them', '.', 'Thanks', '.']
  >>> s = "The colour of the wall is blue."
  >>> StanfordTokenizer(options={"americanize": True}).tokenize(s) # doctest: +SKIP
  ['The', 'color', 'of', 'the', 'wall', 'is', 'blue', '.']
docstr: Use stanford tokenizer's PTBTokenizer to tokenize multiple sentences.
docstr_tok: ["Use","stanford","tokenizer","'s","PTBTokenizer","to","tokenize","multiple","sentences","."]
code: def tokenize(self, s):
      """
      Use stanford tokenizer's PTBTokenizer to tokenize multiple sentences.
      """
      cmd = ["edu.stanford.nlp.process.PTBTokenizer"]
      return self._parse_tokenized_output(self._execute(cmd, s))
  
file_path: nltk/nltk/tokenize/stanford.py
imports: import json
  import os
  import tempfile
  import warnings
  from subprocess import PIPE
  from nltk.internals import _java_options, config_java, find_jar, java
  from nltk.parse.corenlp import CoreNLPParser
  from nltk.tokenize.api import TokenizerI
label_desc: Use image node_id 3 for calling the StanfordTokenizer obj's underlying member method code with example usage: obj.tokenize(s) and returns: self