code: def clean_text(x):
      """
      ###############################################################################
      ## This cleans text string. Use it only as a Series.map(clean_text) function  #
      ###############################################################################
      Input must be one text string only. Don't send arrays or dataframes.
      Clean steps cleans one tweet at a time using following steps:
      1. removes URL
      2. Removes a very small list of stop words - about 65
      """
      x = expandSlangs(
          x
      )  ### do this before lowering case since case is important for sentiment
      x = expandAbbreviations(
          x
      )  ### this is before lowering case since case is important in sentiment
      x = expandContractions(
          x
      )  ### this is after lowering case - just to double check
      x = remove_stopwords(
          x
      )  ## this works well to remove a small number of stop words
      x = remove_punctuations(
          x
      )  # this works well to remove punctuations and add spaces correctly
      x = split_into_lemmas(
          x
      )  ## this lemmatizes text and gets it ready for wordclouds ###
      return x
  
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 18 for calling a global function with example usage: clean_text(x) and returns: x