code: def plot_local_imp(parsed_sentence, word_importances, max_alpha=0.5):
      """Plots the top importances for a parsed sentence when corresponding
          importances are available.
          Internal fast prototyping tool for easy visualization.
          Serves as a visual proxy for dashboard.
  
      Arguments:
          parsed_sentence {[list]} -- Raw text parsed as list with individual raw
          features.
          word_importances {[list]} -- Importances with 1:1 mapping to parsed
          sentences.
  
      Keyword Arguments:
          max_alpha {float} -- Changes intensity of coloring returned by the tool.
              (default: {0.5})
      """
  
      # Prevent special characters like & and < to cause the browser...
      # to display something other than what you intended.
      def html_escape(text):
          return html.escape(text)
  
      word_importances = (
          100.0 * word_importances / (np.sum(np.abs(word_importances)))
      )
  
      highlighted_text = []
      for i, word in enumerate(parsed_sentence):
          weight = word_importances[i]
          if weight > 0:
              highlighted_text.append(
                  '<span style="background-color:rgba(135,206,250,'
                  + str(abs(weight) / max_alpha)
                  + ');">'
                  + html_escape(word)
                  + "</span>"
              )
          elif weight < 0:
              highlighted_text.append(
                  '<span style="background-color:rgba(250,0,0,'
                  + str(abs(weight) / max_alpha)
                  + ');">'
                  + html_escape(word)
                  + "</span>"
              )
          else:
              highlighted_text.append(word)
  
      highlighted_text = " ".join(highlighted_text)
      display(HTML(highlighted_text))
  
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 1 for calling a global function with example usage: plot_local_imp(parsed_sentence, word_importances, max_alpha) and returns: html