code: def plot_global_imp(top_words, top_importances, label_name):
      """Plot top 20 global importances as a matplotlib bar graph.
  
      Arguments:
          top_words {list} -- Words with 1:1 mapping to top_importances.
          top_importances {list} -- Top importance values for top words.
          label_name {str} -- Label for which importances are being displayed.
      """
      plt.figure(figsize=(14, 7))
      plt.title(
          "most important words for class label: " + str(label_name),
          fontsize=18,
      )
      plt.bar(
          range(len(top_importances)),
          top_importances,
          color="r",
          align="center",
      )
      plt.xticks(
          range(len(top_importances)),
          top_words,
          rotation=60,
          fontsize=18,
      )
      plt.show()
  
file_path: interpret-text/python/interpret_text/experimental/common/utils_classical.py
imports: import spacy
  import html
  import string
  import numpy
  import matplotlib.pyplot
  from sklearn.preprocessing import LabelEncoder
  from IPython.core.display import display, HTML
  from sklearn.feature_extraction.text import CountVectorizer
label_desc: Use image node_id 2 for calling a global function with example usage: plot_global_imp(top_words, top_importances, label_name) without return types