code: def main(args):
      pyro.set_rng_seed(args.rng_seed)
  
      dataset = generate_data(args)
      obs = dataset["obs"][: args.duration]
  
      # Choose among inference methods.
      if args.enum:
          samples = infer_hmc_enum(args, obs)
      elif args.sequential:
          samples = infer_hmc_cont(continuous_model, args, obs)
      else:
          samples = infer_hmc_cont(vectorized_model, args, obs)
  
      # Evaluate fit.
      evaluate(args, samples)
  
      # Predict latent time series.
      if args.forecast:
          samples = predict(args, obs, samples, truth=dataset["S2I"])
  
      return samples
  
imports: import argparse
  import logging
  import math
  import re
  from collections import OrderedDict
  import torch
  import pyro
  import pyro.distributions
  import pyro.distributions.hmm
  import pyro.poutine
  from pyro.infer import MCMC, NUTS, config_enumerate, infer_discrete
  from pyro.infer.autoguide import init_to_value
  from pyro.ops.special import safe_log
  from pyro.ops.tensor_utils import convolve
  from pyro.util import warn_if_nan
label_desc: Use image node_id 15 for calling a global function with example usage: main(args) and returns: samples