class_docstr: Holds the encryption functions
  can randomly select an encryption function  use on text
  returns:
      {"text": t, "plaintext": c, "cipher": p, "succeeds": False}
  
  where succeeds is whether or not the text is really encrypted or falsely decrypted
  
  Uses Cyclic3's module  generate pseudo random text
class_docstr_tok: ["Holds","the","encryption","functions","can","randomly","select","an","encryption","function","use","on","text","returns",":","{","``","text","''",":","t",",","``","plaintext","''",":","c",",","``","cipher","''",":","p",",","``","succeeds","''",":","False","}","where","succeeds","is","whether","or","not","the","text","is","really","encrypted","or","falsely","decrypted","Uses","Cyclic3","'s","module","generate","pseudo","random","text"]
code: def __init__(self):  # pragma: no cover
      self.methods = [
          self.Base64,
          self.Ascii,
          self.Base16,
          self.Base32,
          self.Binary,
          self.Hex,
          self.MorseCode,
          self.Reverse,
          self.Vigenere,
          self.base58_bitcoin,
          self.base58_ripple,
          self.b62,
      ]
      self.morse_dict = dict(cipheydists.get_translate("morse"))
      self.letters = string.ascii_lowercase
      self.group = cipheydists.get_charset("english")["lcase"]
  
imports: import base64
  import binascii
  import random
  import re
  import string
  import base58
  import base62
  import cipheycore
  import cipheydists
  import nltk
  from nltk.tokenize.treebank import TreebankWordDetokenizer
label_desc: Use image node_id 1 to create a new encipher_crypto object  with example: obj = encipher_crypto()