code: def tokenize(text, to_lower=False, delimiters=DEFAULT_DELIMITERS):
      """
      Tokenize the input SArray of text strings and return the list of tokens.
  
      Parameters
      ----------
      text : SArray[str]
          Input data of strings representing English text. This tokenizer is not
          intended to process XML, HTML, or other structured text formats.
  
      to_lower : bool, optional
          If True, all strings are converted to lower case before tokenization.
  
      delimiters : list[str], None, optional
          Input strings are tokenized using delimiter characters in this list.
          Each entry in this list must contain a single character. If set to
          `None`, then a Penn treebank-style tokenization is used, which contains
          smart handling of punctuations.
  
      Returns
      -------
      out : SArray[list]
          Each text string in the input is mapped to a list of tokens.
  
      See Also
      --------
      count_words, count_ngrams, tf_idf
  
      References
      ----------
      - `Penn treebank tokenization <https://web.archive.org/web/19970614072242/http://www.cis.upenn.edu:80/~treebank/tokenization.html>`_
  
      Examples
      --------
      .. sourcecode:: python
  
          >>> import turicreate
  
          >>> docs = turicreate.SArray(['This is the first sentence.',
                                        "This one, it's the second sentence."])
  
          # Default tokenization by space characters
          >>> turicreate.text_analytics.tokenize(docs)
          dtype: list
          Rows: 2
          [['This', 'is', 'the', 'first', 'sentence.'],
           ['This', 'one,', "it's", 'the', 'second', 'sentence.']]
  
          # Penn treebank-style tokenization
          >>> turicreate.text_analytics.tokenize(docs, delimiters=None)
          dtype: list
          Rows: 2
          [['This', 'is', 'the', 'first', 'sentence', '.'],
           ['This', 'one', ',', 'it', "'s", 'the', 'second', 'sentence', '.']]
  
      """
      _raise_error_if_not_sarray(text, "text")
  
      ## Compute word counts
      sf = _turicreate.SFrame({"docs": text})
      fe = _feature_engineering.Tokenizer(
          features="docs",
          to_lower=to_lower,
          delimiters=delimiters,
          output_column_prefix=None,
      )
      tokens = fe.fit_transform(sf)
  
      return tokens["docs"]
  
imports: from __future__ import print_function
  from __future__ import division
  from __future__ import absolute_import
  import turicreate
  from turicreate import SArray
  from turicreate import SFrame
  from turicreate.toolkits._internal_utils import _raise_error_if_not_sarray
  import random
  import six
  import turicreate.toolkits._feature_engineering
label_desc: Use image node_id 5 for calling a global function with example usage: tokenize(text, to_lower, delimiters) and returns: tokens