code: def draw_word_clouds(
      dft,
      each_string_var,
      chart_format,
      plotname,
      dep,
      problem_type,
      classes,
      mk_dir,
      verbose=0,
  ):
      dft = dft[:]
      width_size = 20
      height_size = 10
      image_count = 0
      imgdata_list = []
  
      if problem_type == "Regression" or problem_type == "Clustering":
          ########## This is for Regression and Clustering problems only #####
          num_plots = 1
          fig = plt.figure(
              figsize=(
                  min(num_plots * width_size, 20),
                  min(num_plots * height_size, 20),
              )
          )
          cols = 2
          rows = int(num_plots / cols + 0.5)
          plotc = 1
          while plotc <= num_plots:
              plt.subplot(rows, cols, plotc)
              ax1 = plt.gca()
              wc1 = draw_wordcloud_from_dataframe(
                  dft,
                  each_string_var,
                  chart_format,
                  dep,
                  mk_dir,
                  verbose,
              )
              plotc += 1
              ax1.axis("off")
              ax1.imshow(wc1)
              ax1.set_title("Wordcloud for %s" % each_string_var)
          image_count = 0
          if verbose == 2:
              imgdata_list.append(
                  save_image_data(
                      fig, chart_format, plotname, depVar, mk_dir
                  )
              )
              image_count += 1
          if verbose <= 1:
              plt.show()
      else:
          ########## This is for Classification problems only ###########
          num_plots = len(classes)
          target_vars = dft[dep].unique()
          fig = plt.figure(
              figsize=(
                  min(num_plots * width_size, 20),
                  min(num_plots * height_size, 20),
              )
          )
          cols = 2
          rows = int(num_plots / cols + 0.5)
          plotc = 1
          while plotc <= num_plots:
              plt.subplot(rows, cols, plotc)
              ax1 = plt.gca()
              ax1.axis("off")
              dft_target = dft.loc[
                  (dft[dep] == target_vars[plotc - 1])
              ][each_string_var]
              if isinstance(dft_target, pd.Series):
                  wc1 = draw_wordcloud_from_dataframe(
                      pd.DataFrame(dft_target),
                      each_string_var,
                      chart_format,
                      dep,
                      mk_dir,
                      verbose,
                  )
              else:
                  wc1 = draw_wordcloud_from_dataframe(
                      dft_target,
                      each_string_var,
                      chart_format,
                      dep,
                      mk_dir,
                      verbose,
                  )
              ax1.imshow(wc1)
              ax1.set_title(
                  "Wordcloud for %s, target=%s"
                  % (each_string_var, target_vars[plotc - 1]),
                  fontsize=20,
              )
              plotc += 1
          fig.tight_layout()
          ### This is where you save the fig or show the fig ######
          image_count = 0
          if verbose == 2:
              imgdata_list.append(
                  save_image_data(
                      fig, chart_format, plotname, depVar, mk_dir
                  )
              )
              image_count += 1
          if verbose <= 1:
              plt.show()
  
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 21 for calling a global function with example usage: draw_word_clouds(dft, each_string_var, chart_format, plotname, dep, problem_type, classes, mk_dir, verbose) without return types