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","."]
code: def __init__(self, model: Model, *args, **kwargs) -> None:
      super().__init__(model, *args, **kwargs)
  
      # Add and run model checks
      self._add_model_softmax_check()
      self._add_model_check(
          lambda layer: not ichecks.only_relu_activation(layer),
          "This DeepTaylor implementation only supports ReLU activations.",
          check_type="exception",
      )
      self._do_model_checks()
  
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 1 to create a new DeepTaylor object from inherited base classes: ReverseAnalyzerBase with example: obj = DeepTaylor(model)