code: def compute(
      target,
      predictions,
      sample_weight,
      ml_task,
      sensitive_features=None,
      fairness_metric=None,
      fairness_threshold=None,
      privileged_groups=[],
      underprivileged_groups=[],
      previous_fairness_optimization=None,
  ):
      with warnings.catch_warnings():
          warnings.simplefilter("ignore")
          if ml_task == BINARY_CLASSIFICATION:
              return AdditionalMetrics.binary_classification(
                  target,
                  predictions,
                  sample_weight,
                  sensitive_features,
                  fairness_metric,
                  fairness_threshold,
                  privileged_groups,
                  underprivileged_groups,
                  previous_fairness_optimization,
              )
          elif ml_task == MULTICLASS_CLASSIFICATION:
              return AdditionalMetrics.multiclass_classification(
                  target,
                  predictions,
                  sample_weight,
                  sensitive_features,
                  fairness_metric,
                  fairness_threshold,
                  privileged_groups,
                  underprivileged_groups,
                  previous_fairness_optimization,
              )
          elif ml_task == REGRESSION:
              return AdditionalMetrics.regression(
                  target,
                  predictions,
                  sample_weight,
                  sensitive_features,
                  fairness_metric,
                  fairness_threshold,
                  privileged_groups,
                  underprivileged_groups,
                  previous_fairness_optimization,
              )
  
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 4 for calling the AdditionalMetrics obj's underlying member method code with example usage: obj.compute(target, predictions, sample_weight, ml_task, sensitive_features, fairness_metric, fairness_threshold, privileged_groups, underprivileged_groups, previous_fairness_optimization) and returns: AdditionalMetrics, AdditionalMetrics, AdditionalMetrics