class_docstr: Gradient analyzer based on build-in gradient.
  
  Returns as analysis the function value with respect to the input.
  The gradient is computed via the build in function.
  Is mainly used for debugging purposes.
  
  :param model: A Keras model.
class_docstr_tok: ["Gradient","analyzer","based","on","build-in","gradient",".","Returns","as","analysis","the","function","value","with","respect","to","the","input",".","The","gradient","is","computed","via","the","build","in","function",".","Is","mainly","used","for","debugging","purposes",".",":","param","model",":","A","Keras","model","."]
code: def _create_analysis(self, model, stop_analysis_at_tensors=None):
      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
      ]
      ret = ibackend.to_list(
          kbackend.gradients(model.outputs[0], tensors_to_analyze)
      )
  
      if self._postprocess == "abs":
          ret = [kbackend.abs(r) for r in ret]
      elif self._postprocess == "square":
          ret = [kbackend.square(r) for r in ret]
  
      return ret
  
imports: from __future__ import annotations
  import tensorflow
  import tensorflow.keras.backend
  import tensorflow.keras.layers
  import innvestigate.backend
  import innvestigate.backend.checks
  import innvestigate.backend.graph
  from innvestigate.analyzer.network_base import AnalyzerNetworkBase
  from innvestigate.analyzer.reverse_base import ReverseAnalyzerBase
  from innvestigate.analyzer.wrapper import GaussianSmoother, PathIntegrator
  from innvestigate.backend.types import List, OptionalList, Tensor
label_desc: Use image node_id 2 for calling the BaselineGradient obj's underlying member method code with example usage: obj._create_analysis(model, stop_analysis_at_tensors) and returns: ret