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 __init__(self, model, low=None, high=None, **kwargs):
      super().__init__(model, **kwargs)
  
      if low is None or high is None:
          raise ValueError(
              "The low or high parameter is missing. "
              "Z-B (bounded rule) require both values."
          )
  
      self._bounds_low = low
      self._bounds_high = high
  
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 BoundedDeepTaylor object from inherited base classes: DeepTaylor with example: obj = BoundedDeepTaylor(model, low, high)