code: def plot_importance(importance_df, figsize=(8, 8), kind="default"):
      """Plot feature importance
  
      Parameters
      ----------
      importance_df : pandas dataframe
          Output dataframe from LOFO/FLOFO get_importance
      kind : string
          plot type can be default or box
      figsize : tuple
      """
      importance_df = importance_df.copy()
      importance_df["color"] = (
          importance_df["importance_mean"] > 0
      ).map({True: "g", False: "r"})
      importance_df.sort_values("importance_mean", inplace=True)
  
      available_kinds = {"default", "box"}
      if kind not in available_kinds:
          warnings.warn(
              "{kind} not in {ak}. Setting to default".format(
                  kind=kind, ak=available_kinds
              )
          )
  
      if kind == "default":
          importance_df.plot(
              x="feature",
              y="importance_mean",
              xerr="importance_std",
              kind="barh",
              color=importance_df["color"],
              figsize=figsize,
          )
      elif kind == "box":
          lofo_score_cols = [
              col
              for col in importance_df.columns
              if col.startswith("val_imp")
          ]
          features = importance_df["feature"].values.tolist()
          importance_df.set_index("feature")[lofo_score_cols].T.boxplot(
              column=features, vert=False, figsize=figsize
          )
  
label_desc: Use image node_id 1 for calling a global function with example usage: plot_importance(importance_df, figsize, kind) without return types