class_docstr: A RBF kernel for use in the SVGD inference algorithm. The bandwidth of the kernel is chosen from the
  particles using a simple heuristic as in reference [1].
  
  :param float bandwidth_factor: Optional factor by which to scale the bandwidth, defaults to 1.0.
  :ivar float ~.bandwidth_factor: Property that controls the factor by which to scale the bandwidth
      at each iteration.
  
  References
  
  [1] "Stein Variational Gradient Descent: A General Purpose Bayesian Inference Algorithm,"
      Qiang Liu, Dilin Wang
docstr: :param float bandwidth_factor: Optional factor by which to scale the bandwidth
docstr_tok: [":","param","float","bandwidth_factor",":","Optional","factor","by","which","to","scale","the","bandwidth"]
code: def __init__(self, bandwidth_factor=None):
      """
      :param float bandwidth_factor: Optional factor by which to scale the bandwidth
      """
      self.bandwidth_factor = bandwidth_factor
  
imports: import math
  from abc import ABCMeta, abstractmethod
  import torch
  from torch.distributions import biject_to
  import pyro
  from pyro import poutine
  from pyro.distributions import Delta
  from pyro.distributions.util import copy_docs_from
  from pyro.infer.autoguide.guides import AutoContinuous
  from pyro.infer.autoguide.initialization import init_to_sample
  from pyro.infer.trace_elbo import Trace_ELBO
label_desc: Use image node_id 1 to create a new RBFSteinKernel object from inherited base classes: SteinKernel with example: obj = RBFSteinKernel(bandwidth_factor)