class_docstr: DeepTaylor for ReLU-networks with unbounded input
  
  This class implements the DeepTaylor algorithm for neural networks with
  ReLU activation and unbounded input ranges.
  
  :param model: A Keras model.
class_docstr_tok: ["DeepTaylor","for","ReLU-networks","with","unbounded","input","This","class","implements","the","DeepTaylor","algorithm","for","neural","networks","with","ReLU","activation","and","unbounded","input","ranges",".",":","param","model",":","A","Keras","model","."]
docstr: Block all default mappings.
docstr_tok: ["Block","all","default","mappings","."]
code: def _default_reverse_mapping(
      self, _Xs, _Ys, _reversed_Ys, reverse_state
  ):
      """
      Block all default mappings.
      """
      raise NotImplementedError(
          f"""Layer {reverse_state["layer"]} not supported."""
      )
  
imports: from __future__ import annotations
  from typing import Any
  import tensorflow.keras.layers
  import tensorflow.keras.models
  import innvestigate.analyzer.relevance_based.relevance_rule
  import innvestigate.backend.checks
  import innvestigate.backend.graph
  from innvestigate.analyzer.reverse_base import ReverseAnalyzerBase
  from innvestigate.backend.types import Model
label_desc: Use image node_id 3 for calling the DeepTaylor obj's underlying member method code with example usage: obj._default_reverse_mapping(_Xs, _Ys, _reversed_Ys, reverse_state) without return types