code: def return_stop_words():
      from nltk.corpus import stopwords
  
      STOP_WORDS = [
          "it",
          "this",
          "that",
          "to",
          "its",
          "am",
          "is",
          "are",
          "was",
          "were",
          "a",
          "an",
          "the",
          "and",
          "or",
          "of",
          "at",
          "by",
          "for",
          "with",
          "about",
          "between",
          "into",
          "above",
          "below",
          "from",
          "up",
          "down",
          "in",
          "out",
          "on",
          "over",
          "will",
          "shall",
          "could",
          "under",
          "again",
          "further",
          "then",
          "once",
          "all",
          "any",
          "both",
          "each",
          "would",
          "few",
          "more",
          "most",
          "other",
          "some",
          "such",
          "only",
          "own",
          "same",
          "so",
          "than",
          "too",
          "very",
          "s",
          "t",
          "can",
          "just",
          "d",
          "ll",
          "m",
          "o",
          "re",
          "ve",
          "y",
          "ain",
          "ma",
          "them",
          "themselves",
          "they",
          "he",
          "she",
          "ex",
          "become",
          "their",
      ]
      add_words = [
          "s",
          "m",
          "you",
          "not",
          "get",
          "no",
          "via",
          "one",
          "still",
          "us",
          "u",
          "hey",
          "hi",
          "oh",
          "jeez",
          "the",
          "a",
          "in",
          "to",
          "of",
          "i",
          "and",
          "is",
          "for",
          "on",
          "it",
          "got",
          "aww",
          "awww",
          "not",
          "my",
          "that",
          "by",
          "with",
          "are",
          "at",
          "this",
          "from",
          "be",
          "have",
          "was",
          "",
          " ",
          "say",
          "s",
          "u",
          "ap",
          "afp",
          "...",
          "n",
          "\\",
      ]
      # stopWords = text.ENGLISH_STOP_WORDS.union(add_words)
      stop_words = list(set(STOP_WORDS + add_words))
      excl = [
          "will",
          "i'll",
          "shall",
          "you'll",
          "may",
          "don't",
          "hadn't",
          "hasn't",
          "haven't",
          "don't",
          "isn't",
          "if",
          "mightn't",
          "mustn'",
          "mightn't",
          "mightn",
          "needn't",
          "needn",
          "needn't",
          "no",
          "not",
          "shan",
          "shan't",
          "shouldn",
          "shouldn't",
          "wasn't",
          "wasn",
          "weren",
          "weren't",
          "won",
          "won't",
          "wouldn",
          "wouldn't",
          "you'd",
          "you'd",
          "you'll",
          "you're",
          "yourself",
          "yourselves",
      ]
      stopWords = left_subtract(stop_words, excl)
      return sorted(stopWords)
  
imports: import pandas, numpy
  import pdb
  import nltk
  from nltk.sentiment.vader import SentimentIntensityAnalyzer
  from operator import itemgetter
  import copy
  from nltk import word_tokenize, pos_tag
  from collections import Counter
  from nltk.stem.wordnet import WordNetLemmatizer
  from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer
  import emoji
  import string
  import string
  from nltk.tokenize import word_tokenize
  import re
  import string
  import re
  from wordcloud import WordCloud, STOPWORDS
  import matplotlib.pyplot
  from textblob import TextBlob, Word
  from itertools import chain
label_desc: Use image node_id 2 for calling a global function with example usage: return_stop_words() and returns: sorted