code: def include_revision(revision_num, skip_factor=1.1):
      """Decide whether to include a revision.
  
      If the number of revisions is large, we exclude some revisions to avoid
      a quadratic blowup in runtime, since the article is likely also large.
  
      We make the ratio between consecutive included revision numbers
      appproximately equal to "factor".
  
      Args:
        revision_num: an integer
        skip_factor: a floating point number >= 1.0
  
      Returns:
        a boolean
      """
      if skip_factor <= 1.0:
          return True
      return int(
          math.log1p(revision_num) / math.log(skip_factor)
      ) != int(math.log(revision_num + 2.0) / math.log(skip_factor))
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import math
  import os
  import random
  import re
  import subprocess
  from tensor2tensor.data_generators import generator_utils
  from tensor2tensor.data_generators import text_encoder
  import tensorflow.compat.v1
label_desc: Use image node_id 2 for calling a global function with example usage: include_revision(revision_num, skip_factor) and returns: unknown, True