class_docstr: Computes a vector of smooth labels from a vector of hard ones. The hard labels have to contain ones for the
  correct classes and zeros for all the others. The remaining probability mass between `max_value` and 1 is
  distributed uniformly between the incorrect classes for each instance.
  
  
  | Paper link: https://pdfs.semanticscholar.org/b5ec/486044c6218dd41b17d8bba502b32a12b91a.pdf
  
  | Please keep in mind the limitations of defences. For details on how to evaluate classifier security in general,
      see https://arxiv.org/abs/1902.06705  .
code: def _check_params(self) -> None:
      if self.max_value <= 0 or self.max_value > 1:
          raise ValueError(
              "The maximum value for correct labels must be between 0 and 1."
          )
  
file_path: adversarial-robustness-toolbox/art/defences/preprocessor/label_smoothing.py
imports: from __future__ import absolute_import, division, print_function, unicode_literals
  import logging
  from typing import Optional, Tuple
  import numpy
  from art.defences.preprocessor.preprocessor import Preprocessor
label_desc: Use image node_id 3 for calling the LabelSmoothing obj's underlying member method code with example usage: obj._check_params() without return types