class_docstr: Implement the local spatial smoothing defence approach in PyTorch.
  
  | 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.window_size, int) and self.window_size > 0
      ):
          raise ValueError(
              "Sliding window size must be a positive integer."
          )
  
      if self.clip_values is not None and len(self.clip_values) != 2:
          raise ValueError(
              "'clip_values' should be a tuple of 2 floats or arrays containing the allowed data range."
          )
  
      if (
          self.clip_values is not None
          and 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/spatial_smoothing_pytorch.py
imports: from __future__ import absolute_import, division, print_function, unicode_literals
  import logging
  from typing import Optional, Tuple, TYPE_CHECKING
  import numpy
  from art.defences.preprocessor.preprocessor import PreprocessorPyTorch
label_desc: Use image node_id 3 for calling the SpatialSmoothingPyTorch obj's underlying member method code with example usage: obj._check_params() without return types