code: def __init__(self, y: Series, y_uncleaned: Series):
      super().__init__(y)
      self.problem_type_transform = MULTICLASS
      y = self._convert_to_valid_series(y)
      y_uncleaned = self._convert_to_valid_series(y_uncleaned)
      self.cat_mappings_dependent_var: dict = (
          self._generate_categorical_mapping(y)
      )
      self.inv_map: dict = {
          v: k for k, v in self.cat_mappings_dependent_var.items()
      }
  
      self.cat_mappings_dependent_var_uncleaned: dict = (
          self._generate_categorical_mapping(y_uncleaned)
      )
      self.inv_map_uncleaned: dict = {
          v: k
          for k, v in self.cat_mappings_dependent_var_uncleaned.items()
      }
  
      self.num_classes = len(self.cat_mappings_dependent_var.keys())
      self.ordered_class_labels = list(
          y_uncleaned.astype("category").cat.categories
      )
      self.valid_ordered_class_labels = list(
          y.astype("category").cat.categories
      )
      self.ordered_class_labels_transformed = list(
          range(len(self.valid_ordered_class_labels))
      )
      self.invalid_class_count = len(self.ordered_class_labels) - len(
          self.valid_ordered_class_labels
      )
      self.labels_to_zero_fill = [
          1 if label not in self.valid_ordered_class_labels else 0
          for label in self.ordered_class_labels
      ]
      self.label_index_to_keep = [
          i
          for i, label in enumerate(self.labels_to_zero_fill)
          if label == 0
      ]
      self.label_index_to_remove = [
          i
          for i, label in enumerate(self.labels_to_zero_fill)
          if label == 1
      ]
  
imports: import copy
  import logging
  from typing import Union
  import numpy
  from pandas import DataFrame, Series
  from autogluon.core.constants import BINARY, MULTICLASS, QUANTILE, REGRESSION, SOFTCLASS
  from autogluon.core.utils import get_pred_from_proba, get_pred_from_proba_df
label_desc: Use image node_id 1 to create a new LabelCleanerMulticlass object from inherited base classes: LabelCleaner with example: obj = LabelCleanerMulticlass(y, y_uncleaned)