code: def apply(self, Xs, Ys, Rs, reverse_state: dict) -> List[Tensor]:
      # Apply relus conditioned on backpropagated values.
      Rs = ibackend.apply(self._activation, Rs)
  
      # Apply gradient of forward pass without relus.
      Ys_wo_relu = ibackend.apply(self._layer_wo_relu, Xs)
      return ibackend.gradients(Xs, Ys_wo_relu, Rs)
  
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 DeconvnetReverseReLULayer obj's underlying member method code with example usage: obj.apply(Xs, Ys, Rs, reverse_state) and returns: ibackend