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","."]
docstr: To be theoretically sound Deep-Taylor expects only positive outputs.
docstr_tok: ["To","be","theoretically","sound","Deep-Taylor","expects","only","positive","outputs","."]
code: def _prepare_model(self, model):
      """
      To be theoretically sound Deep-Taylor expects only positive outputs.
      """
  
      positive_outputs = [klayers.ReLU()(x) for x in model.outputs]
      model_with_positive_output = kmodels.Model(
          inputs=model.inputs, outputs=positive_outputs
      )
  
      return super()._prepare_model(model_with_positive_output)
  
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 DeepTaylor obj's underlying member method code with example usage: obj._prepare_model(model) and returns: super