class_docstr: Implementation of a postprocessor based on adding class labels to classifier output.
class_docstr_tok: ["Implementation","of","a","postprocessor","based","on","adding","class","labels","to","classifier","output","."]
docstr: Create a ClassLabels postprocessor.
  
  :param apply_fit: True if applied during fitting/training.
  :param apply_predict: True if applied during predicting.
docstr_tok: ["Create","a","ClassLabels","postprocessor",".",":","param","apply_fit",":","True","if","applied","during","fitting\/training",".",":","param","apply_predict",":","True","if","applied","during","predicting","."]
code: def __init__(
      self, apply_fit: bool = False, apply_predict: bool = True
  ) -> None:
      """
      Create a ClassLabels postprocessor.
  
      :param apply_fit: True if applied during fitting/training.
      :param apply_predict: True if applied during predicting.
      """
      super().__init__(
          is_fitted=True,
          apply_fit=apply_fit,
          apply_predict=apply_predict,
      )
  
imports: import logging
  import numpy
  from art.defences.postprocessor.postprocessor import Postprocessor
label_desc: Use image node_id 1 to create a new ClassLabels object from inherited base classes: Postprocessor with example: obj = ClassLabels(apply_fit, apply_predict)