code: def __init__(
      self,
      model,
      base_explainer=None,
      stdev_spread=0.15,
      nsamples=25,
      magnitude=True,
  ):
      self.base_explainer = base_explainer or VanillaGradExplainer(
          model
      )
      self.stdev_spread = stdev_spread
      self.nsamples = nsamples
      self.magnitude = magnitude
  
imports: import numpy
  from torch.autograd import Variable, Function
  import torch
  import types
label_desc: Use image node_id 1 to create a new SmoothGradExplainer object from inherited base classes: object with example: obj = SmoothGradExplainer(model, base_explainer, stdev_spread, nsamples, magnitude)