code: def text2tokens(raw_text):
      """Split the raw_text string into a list of stemmed tokens."""
      clean_text = raw_text.lower().translate(translate_tab)
      tokens = [
          token.strip() for token in tokenizer.tokenize(clean_text)
      ]
      tokens = [token for token in tokens if token not in eng_stopwords]
      stemmed_tokens = [stemmer.stem(token) for token in tokens]
      return [token for token in stemmed_tokens if len(token) > 2]
  
file_path: gensim/docs/src/auto_examples/howtos/run_compare_lda.py
imports: import logging
  from string import punctuation
  from nltk import RegexpTokenizer
  from nltk.stem.porter import PorterStemmer
  from nltk.corpus import stopwords
  from sklearn.datasets import fetch_20newsgroups
  from gensim.corpora import Dictionary
  from gensim.models import LdaMulticore
  import numpy
label_desc: Use image node_id 1 for calling a global function with example usage: text2tokens(raw_text) and returns: unknown