code: def plot_curves(
      curves, curve_names, top_curves_shown=None, plot_mean=True
  ):
      """Temporal importance curve diagram generator for interval forests."""
      # find attributes to display by max information gain for any time point.
      _check_soft_dependencies("matplotlib")
  
      import matplotlib.pyplot as plt
  
      top_curves_shown = (
          len(curves) if top_curves_shown is None else top_curves_shown
      )
      max_ig = [max(i) for i in curves]
      top = sorted(
          range(len(max_ig)), key=lambda i: max_ig[i], reverse=True
      )[:top_curves_shown]
  
      top_curves = [curves[i] for i in top]
      top_names = [curve_names[i] for i in top]
  
      # plot curves with highest max and the mean information gain for each time point if
      # enabled.
      for i in range(0, top_curves_shown):
          plt.plot(
              top_curves[i],
              label=top_names[i],
          )
      if plot_mean:
          plt.plot(
              list(np.mean(curves, axis=0)),
              "--",
              linewidth=3,
              label="Mean Information Gain",
          )
      plt.legend(
          bbox_to_anchor=(0.0, 1.02, 1.0, 0.102),
          loc="lower left",
          ncol=2,
          mode="expand",
          borderaxespad=0.0,
      )
      plt.xlabel("Time Point")
      plt.ylabel("Information Gain")
  
      return plt
  
imports: import numpy
  from sktime.classification.interval_based import CanonicalIntervalForest
  from sktime.transformations.panel import catch22
  from sktime.utils.validation._dependencies import _check_soft_dependencies
label_desc: Use image node_id 1 for calling a global function with example usage: plot_curves(curves, curve_names, top_curves_shown, plot_mean) and returns: plt