class_docstr: Reduces the sensibility of the features of a sample.
  
  | Paper link: https://arxiv.org/abs/1704.01155
  
  | Please keep in mind the limitations of defences. For more information on the limitations of this defence,
      see https://arxiv.org/abs/1803.09868 . For details on how to evaluate classifier security in general,
      see https://arxiv.org/abs/1902.06705
class_docstr_tok: ["Reduces","the","sensibility","of","the","features","of","a","sample",".","|","Paper","link",":","https",":","\/\/arxiv.org\/abs\/1704.01155","|","Please","keep","in","mind","the","limitations","of","defences",".","For","more","information","on","the","limitations","of","this","defence",",","see","https",":","\/\/arxiv.org\/abs\/1803.09868",".","For","details","on","how","to","evaluate","classifier","security","in","general",",","see","https",":","\/\/arxiv.org\/abs\/1902.06705"]
code: def _check_params(self) -> None:
      if (
          not isinstance(self.bit_depth, int)
          or self.bit_depth <= 0
          or self.bit_depth > 64
      ):
          raise ValueError("The bit depth must be between 1 and 64.")
  
      if len(self.clip_values) != 2:
          raise ValueError(
              "`clip_values` should be a tuple of 2 floats containing the allowed data range."
          )
  
      if np.array(self.clip_values[0] >= self.clip_values[1]).any():
          raise ValueError("Invalid `clip_values`: min >= max.")
  
file_path: adversarial-robustness-toolbox/art/defences/preprocessor/feature_squeezing.py
imports: from __future__ import absolute_import, division, print_function, unicode_literals
  import logging
  from typing import Optional, Tuple
  import numpy
  from art.utils import CLIP_VALUES_TYPE
  from art.defences.preprocessor.preprocessor import Preprocessor
label_desc: Use image node_id 3 for calling the FeatureSqueezing obj's underlying member method code with example usage: obj._check_params() without return types