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 _state_to_kwargs(cls, state):
      reference_inputs = state.pop("reference_inputs")
      # call super after popping class-specific states:
      kwargs = super()._state_to_kwargs(state)
  
      # We use steps instead.
      kwargs.update(
          {
              "reference_inputs": reference_inputs,
              "steps": kwargs["augment_by_n"],
          }
      )
      del kwargs["augment_by_n"]
      return kwargs
  
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 8 for calling the PathIntegrator obj's underlying member method code with example usage: obj._state_to_kwargs(cls, state) and returns: kwargs