class_docstr: Gradient analyzer.
  
  Returns as analysis the function value with respect to the input.
  The gradient is computed via the librarie's network reverting.
  
  :param model: A Keras model.
class_docstr_tok: ["Gradient","analyzer",".","Returns","as","analysis","the","function","value","with","respect","to","the","input",".","The","gradient","is","computed","via","the","librarie","'s","network","reverting",".",":","param","model",":","A","Keras","model","."]
code: def _postprocess_analysis(
      self, Xs: OptionalList[Tensor]
  ) -> List[Tensor]:
      ret = super()._postprocess_analysis(Xs)
  
      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 3 for calling the Gradient obj's underlying member method code with example usage: obj._postprocess_analysis(Xs) and returns: ret