class_docstr: DeepTaylor for ReLU-networks with bounded input
  
  This class implements the DeepTaylor algorithm for neural networks with
  ReLU activation and bounded input ranges.
  
  :param model: A Keras model.
  :param low: Lowest value of the input range. See Z_B rule.
  :param high: Highest value of the input range. See Z_B rule.
class_docstr_tok: ["DeepTaylor","for","ReLU-networks","with","bounded","input","This","class","implements","the","DeepTaylor","algorithm","for","neural","networks","with","ReLU","activation","and","bounded","input","ranges",".",":","param","model",":","A","Keras","model",".",":","param","low",":","Lowest","value","of","the","input","range",".","See","Z_B","rule",".",":","param","high",":","Highest","value","of","the","input","range",".","See","Z_B","rule","."]
code: def _state_to_kwargs(cls, state):
      low = state.pop("low")
      high = state.pop("high")
  
      # call super after popping class-specific states:
      kwargs = super()._state_to_kwargs(state)
  
      kwargs.update({"low": low, "high": high})
      return kwargs
  
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 4 for calling the BoundedDeepTaylor obj's underlying member method code with example usage: obj._state_to_kwargs(cls, state) and returns: kwargs