code: def model(data, k):
      latent = named.Object("latent")
  
      # Create parameters for a Gaussian mixture model.
      latent.probs.param_(
          torch.ones(k) / k, constraint=constraints.simplex
      )
      latent.locs.param_(torch.zeros(k))
      latent.scales.param_(
          torch.ones(k), constraint=constraints.positive
      )
  
      # Observe all the data. We pass a local latent in to the local_model.
      latent.local = named.List()
      for x in data:
          local_model(
              latent.local.add(),
              latent.probs,
              latent.locs,
              latent.scales,
              obs=x,
          )
  
file_path: pyro/examples/contrib/autoname/mixture.py
imports: import argparse
  import torch
  from torch.distributions import constraints
  import pyro
  import pyro.distributions
  from pyro.contrib.autoname import named
  from pyro.infer import SVI, JitTrace_ELBO, Trace_ELBO
  from pyro.optim import Adam
label_desc: Use image node_id 1 for calling a global function with example usage: model(data, k) without return types