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: Compute minhash, cached.
docstr_tok: ["Compute","minhash",",","cached","."]
code: def find_minhash(self, num_perm=128):
      """
      Compute minhash, cached.
      """
      words = self.words
      doc_hash = MinHash(num_perm=num_perm)
      for word, _ in words:
          doc_hash.update(word.encode("utf8"))
      return list(doc_hash.digest())
  
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 24 for calling the Doc obj's underlying member method code with example usage: obj.find_minhash(num_perm) and returns: list