code: def filter_paragraph(p):
      """Simple filter to remove obviously bad paragraphs (bad text extraction).
  
      Note this needs to run very quickly as it is applied to every paragraph
      in the corpus, so nothing fancy! This whole method should be linear
      expected time in len(p).
  
      Args:
        p: string, paragraph
  
      Returns:
        True if we should remove the paragraph.
      """
      # Expect a minimum number of words.
      tokens = p.split()
      if len(tokens) < 6:
          return True
  
      # Require some letters.
      if not re.search(_SOME_ALPHA_RE, p):
          return True
  
      # Keep this one at the end, probably the most complicated logic.
      # We try to detect sentences, which should have a minimum of 3 tokens
      # with only alphabetic characters.
      last = 0
      found_sentence = False
      num_alpha = 0
      for i, x in enumerate(tokens):
          if x == ".":
              if i - last > 3 and num_alpha >= 3:
                  found_sentence = True
                  break
              last = i
              num_alpha = 0
          if re.match(_ONLY_ALPHA_RE, x):
              num_alpha += 1
      if not found_sentence:
          return True
  
      return False
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import collections
  import contextlib
  import datetime
  import gzip
  import os
  import re
  import urllib
  import tensorflow.compat.v1
label_desc: Use image node_id 9 for calling a global function with example usage: filter_paragraph(p) and returns: False, True, True, True