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 _create_analysis(self, *args, **kwargs):
      low, high = self._bounds_low, self._bounds_high
  
      class BoundedProxyRule(lrp_rules.BoundedRule):
          def __init__(self, *args, **kwargs):
              super().__init__(*args, low=low, high=high, **kwargs)
  
      self._add_conditional_reverse_mapping(
          lambda l: ichecks.is_input_layer(l)
          and ichecks.contains_kernel(l),
          BoundedProxyRule,
          name="deep_taylor_first_layer_bounded",
          priority=10,  # do first
      )
  
      return super()._create_analysis(*args, **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 2 for calling the BoundedDeepTaylor obj's underlying member method code with example usage: obj._create_analysis() and returns: super