code: def fit_guide(guide, args):
      pyro.clear_param_store()
      adam = optim.Adam({"lr": args.learning_rate})
      svi = SVI(model, guide, adam, Trace_ELBO())
      for i in range(args.num_steps):
          loss = svi.step(args.param_a, args.param_b)
          if i % 500 == 0:
              logging.info("[{}]Elbo loss = {:.2f}".format(i, loss))
  
imports: import argparse
  import logging
  import os
  from functools import partial
  import matplotlib.pyplot
  import seaborn
  import torch
  from matplotlib.gridspec import GridSpec
  from torch.distributions.utils import broadcast_all
  import pyro
  import pyro.distributions
  from pyro import optim, poutine
  from pyro.distributions import constraints
  from pyro.distributions.transforms import block_autoregressive, iterated
  from pyro.infer import MCMC, NUTS, SVI, Trace_ELBO
  from pyro.infer.autoguide import AutoDiagonalNormal, AutoNormalizingFlow
  from pyro.infer.reparam import NeuTraReparam
label_desc: Use image node_id 2 for calling a global function with example usage: fit_guide(guide, args) without return types