code: def add_shap_importance(fout, model_path, fold_cnt, repeat_cnt):
      try:
          # SHAP Importance
          imp_data = [
              f
              for f in os.listdir(model_path)
              if "_shap_importance.csv" in f
          ]
          if not len(imp_data):
              return
  
          df_all = []
          for repeat in range(repeat_cnt):
              repeat_str = (
                  f", Repeat {repeat+1}" if repeat_cnt > 1 else ""
              )
              for fold in range(fold_cnt):
                  learner_name = construct_learner_name(
                      fold, repeat, repeat_cnt
                  )
                  fname = learner_name + "_shap_importance.csv"
                  if fname in imp_data:
                      df = pd.read_csv(
                          os.path.join(model_path, fname), index_col=0
                      )
                      df.columns = [f"Learner {fold+1}{repeat_str}"]
                      df_all += [df]
  
          df = pd.concat(df_all, axis=1)
  
          df["m"] = df.mean(axis=1)
          df = df.sort_values(by="m", ascending=False)
          df = df.drop("m", axis=1)
  
          # limit to max 25 features in the plot
          ax = df.head(25).plot.barh(figsize=(10, 7))
          ax.invert_yaxis()
          ax.set_xlabel(
              "mean(|SHAP value|) average impact on model output magnitude"
          )
          fig = ax.get_figure()
          fig.tight_layout(pad=2.0)
          if df.shape[0] > 25:
              ax.set_title("SHAP Top-25 important features")
          else:
              ax.set_title("SHAP feature importance")
          fig.savefig(os.path.join(model_path, "shap_importance.png"))
          fout.write("\n\n## SHAP Importance\n")
          fout.write(f"![SHAP Importance](shap_importance.png)")
      except Exception as e:
          logger.error(
              f"Exception while saving SHAP importance. {str(e)}\nContinuing ..."
          )
  
imports: import copy
  import json
  import logging
  import os
  import time
  import uuid
  import warnings
  import numpy
  import pandas
  from sklearn.metrics import accuracy_score, classification_report, confusion_matrix, f1_score, matthews_corrcoef, mean_absolute_error, mean_absolute_percentage_error, mean_squared_error, precision_score, r2_score, recall_score, roc_auc_score
  from supervised.algorithms.registry import BINARY_CLASSIFICATION, MULTICLASS_CLASSIFICATION, REGRESSION
  from supervised.utils.metric import logloss
  from supervised.utils.config import LOG_LEVEL
  from tabulate import tabulate
  from supervised.fairness.metrics import FairnessMetrics
  from supervised.fairness.report import FairnessReport
  from supervised.fairness.utils import accuracy, false_negative_rate, false_positive_rate, selection_rate, true_negative_rate, true_positive_rate
  from supervised.utils.additional_plots import AdditionalPlots
  from supervised.utils.common import construct_learner_name, get_fold_repeat_cnt
  from supervised.utils.learning_curves import LearningCurves
  from supervised.utils.jsonencoder import MLJSONEncoder
label_desc: Use image node_id 13 for calling the AdditionalMetrics obj's underlying member method code with example usage: obj.add_shap_importance(fout, model_path, fold_cnt, repeat_cnt) without return types