code: def get_random_words(N, a=1, b=20, unique=True):
      words = []
      while len(words) < N:
          length = random.randint(a, b)
          word = get_random_unicode(length)
          if unique and word not in words:
              words.append(word)
          else:
              words.append(word)
      return words
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from __future__ import unicode_literals
  from fasttext import train_supervised
  from fasttext import train_unsupervised
  from fasttext import util
  import fasttext
  import os
  import subprocess
  import unittest
  import tempfile
  import random
  import sys
  import copy
  import numpy
  from fasttext.tests.test_configurations import get_supervised_models
label_desc: Use image node_id 3 for calling a global function with example usage: get_random_words(N, a, b, unique) and returns: words