class_docstr: Create a doc instance of text, obtain cleaned, readable text and
  metadata from this doc.
  
  Properties:
  raw: incoming, unedited text
  language: 2-letter code for the language of the text
  is_detected_language: is the language detected or specified beforehand
  is_reliable_language: is the language specified or was it reliably detected
  hint_language: language you expect your text to be
  _spacy_nlps: nested dictionary {lang: {model_id: model}} with loaded spacy language modules
docstr: returns the lead-3 sentences (text only) of the document
  if the text is smaller than the requested N, return full text
  
  >>> from pprint import pprint
  >>> from textpipe.doc import Doc
  >>> text = '''Rice Pudding - Poem by Alan Alexander Milne.
  ... What is the matter with Mary Jane?
  ... She's crying with all her might and main,
  ... And she won't eat her dinner - rice pudding again.
  ... What is the matter with Mary Jane? '''
  >>> document = Doc(text)
  >>> pprint(document.extract_lead())
  ['Rice Pudding - Poem by Alan Alexander Milne.',
   'What is the matter with Mary Jane?',
   "She's crying with all her might and main, And she won't eat her dinner - "
   'rice pudding again.']
docstr_tok: ["returns","the","lead-3","sentences","(","text","only",")","of","the","document","if","the","text","is","smaller","than","the","requested","N",",","return","full","text",">",">",">","from","pprint","import","pprint",">",">",">","from","textpipe.doc","import","Doc",">",">",">","text","=","``","'Rice","Pudding","-","Poem","by","Alan","Alexander","Milne",".","...","What","is","the","matter","with","Mary","Jane","?","...","She","'s","crying","with","all","her","might","and","main",",","...","And","she","wo","n't","eat","her","dinner","-","rice","pudding","again",".","...","What","is","the","matter","with","Mary","Jane","?","``","'",">",">",">","document","=","Doc","(","text",")",">",">",">","pprint","(","document.extract_lead","(",")",")","[","'Rice","Pudding","-","Poem","by","Alan","Alexander","Milne",".","'",",","'What","is","the","matter","with","Mary","Jane","?","'",",","``","She","'s","crying","with","all","her","might","and","main",",","And","she","wo","n't","eat","her","dinner","-","``","'rice","pudding","again",".","'","]"]
code: def extract_lead(self, nsents=3):
      """
      returns the lead-3 sentences (text only) of the document
      if the text is smaller than the requested N, return full text
  
      >>> from pprint import pprint
      >>> from textpipe.doc import Doc
      >>> text = '''Rice Pudding - Poem by Alan Alexander Milne.
      ... What is the matter with Mary Jane?
      ... She's crying with all her might and main,
      ... And she won't eat her dinner - rice pudding again.
      ... What is the matter with Mary Jane? '''
      >>> document = Doc(text)
      >>> pprint(document.extract_lead())
      ['Rice Pudding - Poem by Alan Alexander Milne.',
       'What is the matter with Mary Jane?',
       "She's crying with all her might and main, And she won't eat her dinner - "
       'rice pudding again.']
      """
      return [s[0] for s in self.sents[:nsents]]
  
imports: import functools
  import re
  import unicodedata
  from collections import Counter
  from urllib.parse import urlparse
  import cld2
  import numpy
  import spacy
  import spacy.matcher
  import textacy
  import textacy.ke
  import textacy.text_utils
  from bs4 import BeautifulSoup
  from datasketch import MinHash
  from gensim.models.keyedvectors import KeyedVectors
  from gensim.summarization.summarizer import summarize
  from textpipe.data.emoji import EMOJI_TO_UNICODE_NAME, EMOJI_TO_SENTIMENT
  from textpipe.wrappers import RedisKeyedVectors
  from textpipe.util import getattr_
label_desc: Use image node_id 34 for calling the Doc obj's underlying member method code with example usage: obj.extract_lead(nsents) and returns: unknown