class_docstr: Integrated the analysis along a path
  
  This analyzer:
  * creates a path from input to reference image.
  * creates `steps` number of intermediate inputs and
    creates an analysis for them.
  * sums the analyses and multiplies them with the input-reference_input.
  
  This wrapper is used to implement Integrated Gradients.
  We refer to the paper for further information.
  
  :param subanalyzer: The analyzer to be wrapped.
  :param steps: Number of steps for integration.
  :param reference_inputs: The reference input.
code: def _keras_set_constant_inputs(self, inputs: list[Tensor]) -> None:
      tmp = [kbackend.variable(X) for X in inputs]
      self._keras_constant_inputs = [
          klayers.Input(tensor=X, shape=X.shape[1:]) for X in tmp
      ]
  
imports: from __future__ import annotations
  import numpy
  import tensorflow.keras.backend
  import tensorflow.keras.layers
  import tensorflow.keras.models
  import innvestigate.backend
  import innvestigate.layers
  from innvestigate.analyzer.base import AnalyzerBase
  from innvestigate.analyzer.network_base import AnalyzerNetworkBase
  from innvestigate.backend.types import OptionalList, Tensor
label_desc: Use image node_id 2 for calling the PathIntegrator obj's underlying member method code with example usage: obj._keras_set_constant_inputs(inputs) without return types