class_docstr: Implementation of a postprocessor based on adding Gaussian noise to classifier output.
code: def _check_params(self) -> None:
      if self.scale <= 0:
          raise ValueError("Standard deviation must be positive.")
  
file_path: adversarial-robustness-toolbox/art/defences/postprocessor/gaussian_noise.py
imports: import logging
  import numpy
  from art.defences.postprocessor.postprocessor import Postprocessor
  from art.utils import is_probability
label_desc: Use image node_id 3 for calling the GaussianNoise obj's underlying member method code with example usage: obj._check_params() without return types