code: def coverage_plot(coverage_scores: list, model_names: list) -> None:
      """
      Plots the coverage for a set of models to compare.
      ----------
      coverage_scores: list
          list of coverage scores in same order as model_names
          example: [0.17, 0.25, 0.76]
      model_names: list
          list of model names in same order as coverage_scores
          example: ['Model A', 'Model B', 'Model C']
      Returns:
      -------
          A coverage plot
      """
      # create palette
      recommender_palette = [
          "#ED2BFF",
          "#14E2C0",
          "#FF9F1C",
          "#5E2BFF",
          "#FC5FA3",
      ]
      sns.set_palette(recommender_palette)
  
      # make barplot
      ax = sns.barplot(x=model_names, y=coverage_scores)
  
      # set labels
      ax.set_title("Catalog Coverage in %")
      ax.set_ylabel("coverage")
  
      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 2 for calling a global function with example usage: coverage_plot(coverage_scores, model_names) without return types