code: def save_binary_classification(
      additional_metrics, model_desc, model_path, fold_cnt, repeat_cnt
  ):
      max_metrics = additional_metrics["max_metrics"].transpose()
      accuracy_threshold_metrics = additional_metrics[
          "accuracy_threshold_metrics"
      ].transpose()
      confusion_matrix = additional_metrics["confusion_matrix"]
      threshold = additional_metrics["threshold"]
  
      fairness_metrics = additional_metrics.get("fairness_metrics")
  
      with open(
          os.path.join(model_path, "README.md"), "w", encoding="utf-8"
      ) as fout:
          fout.write(model_desc)
          fout.write(
              "\n## Metric details\n{}\n\n".format(
                  max_metrics.to_markdown()
              )
          )
          fout.write(
              "\n## Metric details with threshold from accuracy metric\n{}\n\n".format(
                  accuracy_threshold_metrics.to_markdown()
              )
          )
          fout.write(
              "\n## Confusion matrix (at threshold={})\n{}".format(
                  np.round(threshold, 6), confusion_matrix.to_markdown()
              )
          )
  
          if fairness_metrics is not None:
              FairnessReport.save_classification(
                  fairness_metrics, fout, model_path, is_multi=False
              )
  
          AdditionalMetrics.add_learning_curves(fout)
          AdditionalMetrics.add_tree_viz(
              fout, model_path, fold_cnt, repeat_cnt
          )
          AdditionalMetrics.add_linear_coefs(
              fout, model_path, fold_cnt, repeat_cnt
          )
          AdditionalMetrics.add_permutation_importance(
              fout, model_path, fold_cnt, repeat_cnt
          )
  
          plots = additional_metrics.get("additional_plots")
          if plots is not None:
              AdditionalPlots.append(fout, model_path, plots)
  
          AdditionalMetrics.add_shap_importance(
              fout, model_path, fold_cnt, repeat_cnt
          )
          AdditionalMetrics.add_shap_binary(
              fout, model_path, fold_cnt, repeat_cnt
          )
  
          fout.write("\n\n[<< Go back](../README.md)\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 7 for calling the AdditionalMetrics obj's underlying member method code with example usage: obj.save_binary_classification(additional_metrics, model_desc, model_path, fold_cnt, repeat_cnt) without return types