code: def add_linear_coefs(fout, model_path, fold_cnt, repeat_cnt):
      coef_files = [
          f for f in os.listdir(model_path) if "_coefs.csv" in f
      ]
      if not len(coef_files):
          return
  
      # check if multiclass
      df = pd.read_csv(
          os.path.join(model_path, coef_files[0]), index_col=0
      )
      if df.shape[0] > 100:
          return
      multiclass = df.shape[1] > 1
  
      if multiclass:
          fout.write("\n\n## Coefficients\n")
  
          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 + "_coefs.csv"
                  if fname in coef_files:
                      fout.write(
                          f"\n### Coefficients learner #{fold+1}{repeat_str}\n"
                      )
                      df = pd.read_csv(
                          os.path.join(model_path, fname), index_col=0
                      )
                      fout.write(df.to_markdown() + "\n")
  
      else:
          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 + "_coefs.csv"
                  if fname in coef_files:
                      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("m", axis=0, ascending=False)
          df = df.drop("m", axis=1)
  
          fout.write("\n\n## Coefficients\n")
          fout.write(df.to_markdown() + "\n")
  
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 10 for calling the AdditionalMetrics obj's underlying member method code with example usage: obj.add_linear_coefs(fout, model_path, fold_cnt, repeat_cnt) without return types