code: def tokenize(doc):
      """Extract tokens from doc.
  
      This uses a simple regex that matches word characters to break strings
      into tokens. For a more principled approach, see CountVectorizer or
      TfidfVectorizer.
      """
      return (tok.lower() for tok in re.findall(r"\w+", doc))
  
file_path: scikit-learn/examples/text/plot_hashing_vs_dict_vectorizer.py
imports: from sklearn.datasets import fetch_20newsgroups
  import re
  from collections import defaultdict
  from time import time
  from sklearn.feature_extraction import DictVectorizer
  import numpy
  from sklearn.feature_extraction import FeatureHasher
  import matplotlib.pyplot
  from sklearn.feature_extraction.text import CountVectorizer
  from sklearn.feature_extraction.text import HashingVectorizer
  from sklearn.feature_extraction.text import TfidfVectorizer
label_desc: Use image node_id 1 for calling a global function with example usage: tokenize(doc) and returns: unknown