code: def mapk_plot(mapk_scores, model_names, k_range):
      """
      Plots the mean average precision at k for a set of models to compare.
      ----------
      mapk_scores: list of lists
          list of list of map@k scores over k. This lis is in same order as model_names
          example: [[0.17, 0.25, 0.76],[0.2, 0.5, 0.74]]
      model_names: list
          list of model names in same order as coverage_scores
          example: ['Model A', 'Model B']
      k_range: list
          list or array indeitifying all k values in order
          example: [1,2,3,4,5,6,7,8,9,10]
      Returns:
      -------
          A map@k plot
      """
      # create palette
      recommender_palette = [
          "#ED2BFF",
          "#14E2C0",
          "#FF9F1C",
          "#5E2BFF",
          "#FC5FA3",
      ]
      sns.set_palette(recommender_palette)
  
      # lineplot
      mapk_df = pd.DataFrame(
          np.column_stack(mapk_scores), k_range, columns=model_names
      )
      ax = sns.lineplot(data=mapk_df)
      plt.xticks(k_range)
      plt.setp(ax.lines, linewidth=5)
  
      # set labels
      ax.set_title("Mean Average Precision at K (MAP@K) Comparison")
      ax.set_ylabel("MAP@K")
      ax.set_xlabel("K")
      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 6 for calling a global function with example usage: mapk_plot(mapk_scores, model_names, k_range) without return types