code: def intra_list_similarity_plot(
      intra_list_similarity_scores, model_names
  ):
      """
      Plots the intra-list similarity for a set of models to compare.
      ----------
      intra_list_similarity_scores: list
          list of intra-list similarity scores in same order as model_names
          example: [0.13, 0.52, 0.36]
      model_names: list
          list of model names in same order as coverage_scores
          example: ['Model A', 'Model B', 'Model C']
      Returns:
      -------
          A intra-list similarity plot
      """
      # create palette
      recommender_palette = [
          "#ED2BFF",
          "#14E2C0",
          "#FF9F1C",
          "#5E2BFF",
          "#FC5FA3",
      ]
      sns.set_palette(recommender_palette)
  
      # make barplot
      ax = sns.barplot(x=model_names, y=intra_list_similarity_scores)
  
      # set labels
      ax.set_title("Similarity in %")
      ax.set_ylabel("similarity in %")
  
      plt.show()
  
imports: import matplotlib.pyplot
  import numpy
  import pandas
  import plotly.graph_objects
  import seaborn
  from funcsigs import signature
  from matplotlib.lines import Line2D
  from sklearn.metrics import auc, average_precision_score, precision_recall_curve, roc_curve
label_desc: Use image node_id 4 for calling a global function with example usage: intra_list_similarity_plot(intra_list_similarity_scores, model_names) without return types