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 __init__(
      self,
      subanalyzer,
      *args,
      steps: int = 16,
      reference_inputs=0,
      **kwargs,
  ):
      super().__init__(subanalyzer, *args, augment_by_n=steps, **kwargs)
  
      self._reference_inputs = reference_inputs
      self._keras_constant_inputs: list[Tensor] | None = None
  
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 1 to create a new PathIntegrator object from inherited base classes: AugmentReduceBase with example: obj = PathIntegrator(subanalyzer)