code: def transform_labels(self, y, inverse=False, proba=False):
      if inverse:
          if proba:
              y_transformed = (
                  self.label_cleaner.inverse_transform_proba(
                      y=y, as_pandas=True
                  )
              )
          else:
              y_transformed = self.label_cleaner.inverse_transform(y=y)
      else:
          if proba:
              y_transformed = self.label_cleaner.transform_proba(
                  y=y, as_pandas=True
              )
          else:
              y_transformed = self.label_cleaner.transform(y=y)
      return y_transformed
  
imports: from __future__ import annotations
  import copy
  import json
  import logging
  import time
  from collections.abc import Iterable
  from typing import List, Union
  import numpy
  import pandas
  from pandas import DataFrame, Series
  from sklearn.metrics import classification_report
  from autogluon.core.constants import AUTO_WEIGHT, BALANCE_WEIGHT, BINARY, MULTICLASS, QUANTILE, REGRESSION
  from autogluon.core.data.label_cleaner import LabelCleaner, LabelCleanerMulticlassToBinary
  from autogluon.core.learner import AbstractLearner
  from autogluon.core.metrics import Scorer, confusion_matrix, get_metric
  from autogluon.core.models.greedy_ensemble.ensemble_selection import EnsembleSelection
  from autogluon.core.utils import augment_rare_classes, compute_weighted_metric, extract_column, get_leaderboard_pareto_frontier, get_pred_from_proba, get_pred_from_proba_df, infer_problem_type
  from autogluon.features.generators import PipelineFeatureGenerator
label_desc: Use image node_id 39 for calling the AbstractTabularLearner obj's underlying member method code with example usage: obj.transform_labels(y, inverse, proba) and returns: y_transformed