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 _state_to_kwargs(cls, state):
      postprocess = state.pop("postprocess")
      # call super after popping class-specific states:
      kwargs = super()._state_to_kwargs(state)
  
      kwargs.update(
          {
              "postprocess": postprocess,
          }
      )
      return kwargs
  
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 4 for calling the BaselineGradient obj's underlying member method code with example usage: obj._state_to_kwargs(cls, state) and returns: kwargs