class_docstr: Time Series Saliency (TSSaliency) Explainer is a model agnostic saliency explainer
  for time series associate tasks. The TSSaliency supports univariate and multivariate
  use cases. It explains temporal importance of different variates on the model prediction.
  TSSaliency incorporates an integrated gradient method for saliency estimation. The
  saliency measure involves the notion of a base value. For example, the base value can be
  the constant signal with average value. The saliency measure is computed by integrating
  the model sensitivity over a trajectory from the base value to the time series signal. The
  TSSaliency explainer provides variate wise contributions to model prediction at a
  temporal resolution.
  
  References:
      .. [#0] `Mukund Sundararajan et al. "Axiomatic Attribution for Deep Networks"
          <https://arxiv.org/pdf/1703.01365.pdf>`_
docstr: Set parameters for the explainer.
docstr_tok: ["Set","parameters","for","the","explainer","."]
code: def set_params(self, *argv, **kwargs):
      """Set parameters for the explainer."""
      self._config.update(kwargs)
      return self
  
file_path: AIX360/aix360/algorithms/tssaliency/tssaliency.py
imports: import warnings
  import numpy
  from typing import List, Union, Callable
  from aix360.algorithms.tslbbe import TSLocalBBExplainer
  from aix360.algorithms.tssaliency.gradient import mc_gradient_compute
  from aix360.algorithms.tsutils.tsframe import tsFrame, to_np_array
label_desc: Use image node_id 2 for calling the TSSaliencyExplainer obj's underlying member method code with example usage: obj.set_params() and returns: self