class_docstr: Simple gradient-free kernel that utilizes an isotropic gaussian random walk in the unconstrained
  latent space of the model. The step size that controls the variance of the kernel is adapted during
  warm-up with a simple adaptation scheme that targets a user-provided acceptance probability.
  
  :param model: Python callable containing Pyro primitives.
  :param float init_step_size: A positive float that controls the initial step size. Defaults to 0.1.
  :param float target_accept_prob: The target acceptance probability used during adaptation of
      the step size. Defaults to 0.234.
  
  Example:
  
      >>> true_coefs = torch.tensor([1., 2., 3.])
      >>> data = torch.randn(2000, 3)
      >>> dim = 3
      >>> labels = dist.Bernoulli(logits=(true_coefs * data).sum(-1)).sample()
      >>>
      >>> def model(data):
      ...     coefs_mean = torch.zeros(dim)
      ...     coefs = pyro.sample('beta', dist.Normal(coefs_mean, torch.ones(3)))
      ...     y = pyro.sample('y', dist.Bernoulli(logits=(coefs * data).sum(-1)), obs=labels)
      ...     return y
      >>>
      >>> hmc_kernel = RandomWalkKernel(model, init_step_size=0.2)
      >>> mcmc = MCMC(hmc_kernel, num_samples=200, warmup_steps=100)
      >>> mcmc.run(data)
      >>> mcmc.get_samples()['beta'].mean(0)  # doctest: +SKIP
      tensor([ 0.9819,  1.9258,  2.9737])
code: def logging(self):
      return OrderedDict(
          [
              (
                  "step size",
                  "{:.2e}".format(math.exp(self._log_step_size)),
              ),
              ("acc. prob", "{:.3f}".format(self._mean_accept_prob)),
          ]
      )
  
file_path: pyro/pyro/infer/mcmc/rwkernel.py
imports: import math
  from collections import OrderedDict
  import torch
  import pyro
  import pyro.distributions
  from pyro.infer.mcmc.mcmc_kernel import MCMCKernel
  from pyro.infer.mcmc.util import initialize_model
label_desc: Use image node_id 6 for calling the RandomWalkKernel obj's underlying member method code with example usage: obj.logging() and returns: OrderedDict