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: Determine the complexity of text using the Flesch
  reading ease test ranging from 0.0 - 100.0 with 0.0
  being the most difficult to read.
  
  >>> from textpipe.doc import Doc
  >>> doc = Doc('Test sentence for testing text')
  >>> doc.complexity
  83.32000000000004
docstr_tok: ["Determine","the","complexity","of","text","using","the","Flesch","reading","ease","test","ranging","from","0.0","-","100.0","with","0.0","being","the","most","difficult","to","read",".",">",">",">","from","textpipe.doc","import","Doc",">",">",">","doc","=","Doc","(","'Test","sentence","for","testing","text","'",")",">",">",">","doc.complexity","83.32000000000004"]
code: def complexity(self):
      """
      Determine the complexity of text using the Flesch
      reading ease test ranging from 0.0 - 100.0 with 0.0
      being the most difficult to read.
  
      >>> from textpipe.doc import Doc
      >>> doc = Doc('Test sentence for testing text')
      >>> doc.complexity
      83.32000000000004
      """
      if not self._text_stats:
          self._text_stats = textacy.TextStats(self._spacy_doc)
      if self._text_stats.n_syllables == 0:
          return 100
      return self._text_stats.flesch_reading_ease
  
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 19 for calling the Doc obj's underlying member method code with example usage: obj.complexity() and returns: self, int