class_docstr: Wrapper to handle both encoded and decoded labels.
class_docstr_tok: ["Wrapper","to","handle","both","encoded","and","decoded","labels","."]
code: def __init__(self, score_func: Callable, labels: list):
      self.score_func = score_func
      self.labels = tuple(labels) if labels is not None else None
      self.__name__ = score_func.__name__
  
imports: import traceback
  import warnings
  from typing import Callable
  import numpy
  from sklearn.exceptions import FitFailedWarning
  from sklearn.metrics._scorer import _PredictScorer, _ProbaScorer, _ThresholdScorer
  from sklearn.preprocessing import LabelEncoder
  from sklearn.utils import _safe_indexing
  from pycaret.utils.generic import get_label_encoder
label_desc: Use image node_id 1 to create a new EncodedDecodedLabelsScoreFunc object  with example: obj = EncodedDecodedLabelsScoreFunc(score_func, labels)