code: def __init__(self, y: Series, positive_class=None):
      super().__init__(y)
      self.problem_type_transform = BINARY
      y = self._convert_to_valid_series(y)
      self.num_classes = 2
      self.unique_values = list(y.unique())
      if len(self.unique_values) != 2:
          raise AssertionError(
              "y does not contain exactly 2 unique values:",
              self.unique_values,
          )
      try:
          self.unique_values.sort()
      except TypeError:
          # Contains both str and int
          self.unique_values = list(y.unique())
      # TODO: Clean this code, for loop
      pos_class_warning = None
      if positive_class is not None:
          if positive_class not in self.unique_values:
              raise ValueError(
                  f"positive_class is not a valid class: {self.unique_values} (positive_class={positive_class})"
              )
          negative_class = [
              c for c in self.unique_values if c != positive_class
          ][0]
          self.inv_map: dict = {negative_class: 0, positive_class: 1}
      elif (
          str(False) in [str(val) for val in self.unique_values]
      ) and (str(True) in [str(val) for val in self.unique_values]):
          false_val = [
              val
              for val in self.unique_values
              if str(val) == str(False)
          ][
              0
          ]  # may be str or bool
          true_val = [
              val for val in self.unique_values if str(val) == str(True)
          ][
              0
          ]  # may be str or bool
          self.inv_map: dict = {false_val: 0, true_val: 1}
      elif (0 in self.unique_values) and (1 in self.unique_values):
          self.inv_map: dict = {0: 0, 1: 1}
      else:
          self.inv_map: dict = {
              self.unique_values[0]: 0,
              self.unique_values[1]: 1,
          }
          pos_class_warning = (
              f"\tNote: For your binary classification, AutoGluon arbitrarily selected which label-value "
              f"represents positive ({self.unique_values[1]}) vs negative ({self.unique_values[0]}) class.\n"
              "\tTo explicitly set the positive_class, either rename classes to 1 and 0, or specify positive_class in Predictor init."
          )
      poslabel = [
          lbl for lbl in self.inv_map.keys() if self.inv_map[lbl] == 1
      ][0]
      neglabel = [
          lbl for lbl in self.inv_map.keys() if self.inv_map[lbl] == 0
      ][0]
      logger.log(
          20,
          "Selected class <--> label mapping:  class 1 = %s, class 0 = %s"
          % (poslabel, neglabel),
      )
      if pos_class_warning is not None:
          logger.log(20, pos_class_warning)
      self.cat_mappings_dependent_var: dict = {
          v: k for k, v in self.inv_map.items()
      }
      self.ordered_class_labels_transformed = [0, 1]
      self.ordered_class_labels = [
          self.cat_mappings_dependent_var[label_transformed]
          for label_transformed in self.ordered_class_labels_transformed
      ]
  
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 LabelCleanerBinary object from inherited base classes: LabelCleaner with example: obj = LabelCleanerBinary(y, positive_class)