class_docstr: A label encoder that supports specifying a positive class on top of sklearn's LabelEncoder.
class_docstr_tok: ["A","label","encoder","that","supports","specifying","a","positive","class","on","top","of","sklearn","'s","LabelEncoder","."]
docstr: Transform labels back to original encoding.
  
  Parameters
  ----------
  y : ndarray of shape (n_samples,)
      Target values.
  
  Returns
  -------
  y : ndarray of shape (n_samples,)
      Original encoding.
docstr_tok: ["Transform","labels","back","to","original","encoding",".","Parameters","--","--","--","--","--","y",":","ndarray","of","shape","(","n_samples",",",")","Target","values",".","Returns","--","--","--","-","y",":","ndarray","of","shape","(","n_samples",",",")","Original","encoding","."]
code: def inverse_transform(self, y):
      """
      Transform labels back to original encoding.
  
      Parameters
      ----------
      y : ndarray of shape (n_samples,)
          Target values.
  
      Returns
      -------
      y : ndarray of shape (n_samples,)
          Original encoding.
      """
      if self._inverse_mapping is not None:
          y = self._inverse_mapping[y]
  
      return self._le.inverse_transform(y)
  
imports: import json
  import logging
  import re
  from typing import Any, Dict, List, Optional, Union
  import jsonschema
  import numpy
  import pandas
  from omegaconf import DictConfig, OmegaConf
  from sklearn.preprocessing import LabelEncoder
  from ..constants import AUTOMM, END_OFFSET, ENTITY_GROUP, NER_ANNOTATION, PROBABILITY, START_OFFSET
label_desc: Use image node_id 6 for calling the CustomLabelEncoder obj's underlying member method code with example usage: obj.inverse_transform(y) and returns: self