class_docstr: Independent Metropolis-Hastings SMC_kernel
code: def tune(self):
      # Update MVNormal proposal based on the mean and covariance of the
      # tempered posterior.
      cov = np.cov(self.tempered_posterior, ddof=0, rowvar=0)
      cov = np.atleast_2d(cov)
      cov += 1e-6 * np.eye(cov.shape[0])
      if np.isnan(cov).any() or np.isinf(cov).any():
          raise ValueError(
              'Sample covariances not valid! Likely "draws" is too small!'
          )
      mean = np.average(self.tempered_posterior, axis=0)
      self.proposal_dist = multivariate_normal(mean, cov)
  
imports: import abc
  import sys
  import warnings
  from abc import ABC
  from typing import Dict, Union, cast
  import numpy
  import pytensor.tensor
  from pytensor.graph.replace import clone_replace
  from scipy.special import logsumexp
  from scipy.stats import multivariate_normal
  from typing_extensions import TypeAlias
  from pymc.backends.ndarray import NDArray
  from pymc.blocking import DictToArrayBijection
  from pymc.initial_point import make_initial_point_expression
  from pymc.model import Point, modelcontext
  from pymc.pytensorf import compile_pymc, floatX, join_nonshared_inputs, make_shared_replacements
  from pymc.sampling.forward import draw
  from pymc.step_methods.metropolis import MultivariateNormalProposal
  from pymc.vartypes import discrete_types
label_desc: Use image node_id 2 for calling the IMH obj's underlying member method code with example usage: obj.tune() without return types