class_docstr: Returns the input with added zero-mean Gaussian noise as analysis.
  
  :param model: A Keras model.
  :param stddev: The standard deviation of the noise.
class_docstr_tok: ["Returns","the","input","with","added","zero-mean","Gaussian","noise","as","analysis",".",":","param","model",":","A","Keras","model",".",":","param","stddev",":","The","standard","deviation","of","the","noise","."]
code: def _create_analysis(
      self, model, stop_analysis_at_tensors=None
  ) -> list[Tensor]:
      if stop_analysis_at_tensors is None:
          stop_analysis_at_tensors = []
  
      tensors_to_analyze = [
          X
          for X in ibackend.to_list(model.inputs)
          if X not in stop_analysis_at_tensors
      ]
      tensors_with_noise = [
          ibackend.add_gaussian_noise(X, stddev=self._stddev)
          for X in tensors_to_analyze
      ]
      return tensors_with_noise
  
file_path: innvestigate/src/innvestigate/analyzer/misc.py
imports: from __future__ import annotations
  import innvestigate.backend
  import innvestigate.layers
  from innvestigate.analyzer.network_base import AnalyzerNetworkBase
  from innvestigate.backend.types import Tensor
label_desc: Use image node_id 2 for calling the Random obj's underlying member method code with example usage: obj._create_analysis(model, stop_analysis_at_tensors) and returns: tensors_with_noise