code: def plot_cif(
      cif,
      normalise_time_points=False,
      top_curves_shown=None,
      plot_mean=True,
  ):
      """Temporal importance curve diagram generator for the CanonicalIntervalForest."""
      if (
          not isinstance(cif, CanonicalIntervalForest)
          or not cif._is_fitted
      ):
          raise ValueError(
              "Input must be a fitted CanonicalIntervalForest classifier."
          )
  
      curves = cif._temporal_importance_curves(
          normalise_time_points=normalise_time_points
      )
      curves = curves.reshape((25 * cif.n_dims_, cif.series_length_))
      features = catch22.feature_names + [
          "Mean",
          "Standard Deviation",
          "Slope",
      ]
      curve_names = []
      for feature in features:
          for i in range(cif.n_dims_):
              name = (
                  feature
                  if cif.n_dims_ == 1
                  else feature + " Dim " + str(i)
              )
              curve_names.append(name)
      return plot_curves(
          curves,
          curve_names,
          top_curves_shown=top_curves_shown,
          plot_mean=plot_mean,
      )
  
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 2 for calling a global function with example usage: plot_cif(cif, normalise_time_points, top_curves_shown, plot_mean) and returns: plot_curves