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 _create_analysis(self, *args: Any, **kwargs: Any):
      # Kernel layers.
      self._add_conditional_reverse_mapping(
          lambda l: (
              ichecks.contains_kernel(l)
              and ichecks.contains_activation(l)
          ),
          lrp_rules.Alpha1Beta0IgnoreBiasRule,
          name="deep_taylor_kernel_w_relu",
      )
      self._add_conditional_reverse_mapping(
          lambda l: (
              ichecks.contains_kernel(l)
              and not ichecks.contains_activation(l)
          ),
          lrp_rules.WSquareRule,
          name="deep_taylor_kernel_wo_relu",
      )
  
      # ReLU Activation layer
      self._add_conditional_reverse_mapping(
          lambda l: (
              not ichecks.contains_kernel(l)
              and ichecks.only_relu_activation(l)
          ),
          self._gradient_reverse_mapping,
          name="deep_taylor_relu",
      )
  
      # Assume conv layer beforehand -> unbounded
      bn_mapping = igraph.apply_mapping_to_fused_bn_layer(
          lrp_rules.WSquareRule,
          fuse_mode="one_linear",
      )
      self._add_conditional_reverse_mapping(
          ichecks.is_batch_normalization_layer,
          bn_mapping,
          name="deep_taylor_batch_norm",
      )
      # Special layers.
      self._add_conditional_reverse_mapping(
          ichecks.is_max_pooling,
          self._gradient_reverse_mapping,
          name="deep_taylor_max_pooling",
      )
      self._add_conditional_reverse_mapping(
          ichecks.is_average_pooling,
          self._gradient_reverse_mapping,
          name="deep_taylor_average_pooling",
      )
      self._add_conditional_reverse_mapping(
          lambda l: isinstance(l, klayers.Add),
          # Ignore scaling with 0.5
          self._gradient_reverse_mapping,
          name="deep_taylor_add",
      )
      self._add_conditional_reverse_mapping(
          lambda l: isinstance(
              l,
              (
                  klayers.UpSampling1D,
                  klayers.UpSampling2D,
                  klayers.UpSampling3D,
                  klayers.Dropout,
                  klayers.SpatialDropout1D,
                  klayers.SpatialDropout2D,
                  klayers.SpatialDropout3D,
              ),
          ),
          self._gradient_reverse_mapping,
          name="deep_taylor_special_layers",
      )
  
      # Layers w/o transformation
      self._add_conditional_reverse_mapping(
          lambda l: isinstance(
              l,
              (
                  klayers.InputLayer,
                  klayers.Cropping1D,
                  klayers.Cropping2D,
                  klayers.Cropping3D,
                  klayers.ZeroPadding1D,
                  klayers.ZeroPadding2D,
                  klayers.ZeroPadding3D,
                  klayers.Concatenate,
                  klayers.Flatten,
                  klayers.Masking,
                  klayers.Permute,
                  klayers.RepeatVector,
                  klayers.Reshape,
              ),
          ),
          self._gradient_reverse_mapping,
          name="deep_taylor_no_transform",
      )
  
      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 DeepTaylor obj's underlying member method code with example usage: obj._create_analysis() and returns: super