code: def guided_backprop_reverse_relu_layer(
      Xs, Ys, reversed_Ys, _reverse_state: dict
  ):
      activation = klayers.Activation("relu")
      # Apply relus conditioned on backpropagated values.
      reversed_Ys = ibackend.apply(activation, reversed_Ys)
  
      # Apply gradient of forward pass.
      return ibackend.gradients(Xs, Ys, reversed_Ys)
  
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 1 for calling a global function with example usage: guided_backprop_reverse_relu_layer(Xs, Ys, reversed_Ys, _reverse_state) and returns: ibackend