code: def multiclass_classification(
      target,
      predictions,
      sample_weight=None,
      sensitive_features=None,
      fairness_metric=None,
      fairness_threshold=None,
      privileged_groups=[],
      underprivileged_groups=[],
      previous_fairness_optimization=None,
  ):
      all_labels = [i[11:] for i in predictions.columns.tolist()[:-1]]
  
      predicted_probas = predictions[predictions.columns[:-1]]
      ll = logloss(
          target,
          predictions[predictions.columns[:-1]],
          sample_weight=sample_weight,
      )
  
      if "target" in target.columns.tolist():
          # multiclass coding with integer
          labels = {i: l for i, l in enumerate(all_labels)}
          target = target["target"].map(labels)
      else:
          # multiclass coding with one-hot encoding
          old_columns = target.columns
          t = target[old_columns[0]]
          for l in all_labels:
              t[target[f"target_{l}"] == 1] = l
  
          target = pd.DataFrame({"target": t})
  
      # Print the confusion matrix
      predicted_labels = predictions["label"]
      predictions = predictions["label"]
      if not pd.api.types.is_string_dtype(predictions):
          predictions = predictions.astype(str)
  
      if not pd.api.types.is_string_dtype(target):
          target = target.astype(str)
  
      conf_matrix = confusion_matrix(
          target,
          predictions,
          labels=all_labels,
          sample_weight=sample_weight,
      )
  
      rows = [f"Predicted as {a}" for a in all_labels]
      cols = [f"Labeled as {a}" for a in all_labels]
  
      conf_matrix = pd.DataFrame(conf_matrix, columns=rows, index=cols)
  
      max_metrics = classification_report(
          target,
          predictions,
          digits=6,
          labels=all_labels,
          output_dict=True,
          sample_weight=sample_weight,
      )
      max_metrics["logloss"] = ll
  
      metrics = {
          "max_metrics": pd.DataFrame(max_metrics).transpose(),
          "confusion_matrix": conf_matrix,
          "additional_plots": AdditionalPlots.plots_multiclass(
              target, predicted_labels, predicted_probas
          ),
      }
  
      if sensitive_features is not None:
          metrics[
              "fairness_metrics"
          ] = FairnessMetrics.multiclass_classification(
              target,
              predicted_labels,
              sensitive_features,
              fairness_metric,
              fairness_threshold,
              privileged_groups,
              underprivileged_groups,
              previous_fairness_optimization,
          )
      return metrics
  
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 2 for calling the AdditionalMetrics obj's underlying member method code with example usage: obj.multiclass_classification(target, predictions, sample_weight, sensitive_features, fairness_metric, fairness_threshold, privileged_groups, underprivileged_groups, previous_fairness_optimization) and returns: metrics