code: def global_model(population):
      tau = args.recovery_time  # Assume this can be measured exactly.
      R0 = pyro.sample("R0", dist.LogNormal(0.0, 1.0))
      rho = pyro.sample("rho", dist.Uniform(0, 1))
  
      # Convert interpretable parameters to distribution parameters.
      rate_s = -R0 / (tau * population)
      prob_i = 1 / (1 + tau)
  
      return rate_s, prob_i, rho
  
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 1 for calling a global function with example usage: global_model(population) and returns: rate_s,  prob_i,  rho