code: def local_model(weights, locs, scale, data):
      assignment = pyro.sample(
          "assignment", dist.Categorical(weights).expand_by([len(data)])
      )
      return pyro.sample(
          "obs", dist.Normal(locs[assignment], scale), obs=data
      )
  
file_path: pyro/examples/contrib/autoname/scoping_mixture.py
imports: import argparse
  import torch
  from torch.distributions import constraints
  import pyro
  import pyro.distributions
  import pyro.optim
  from pyro.contrib.autoname import scope
  from pyro.infer import SVI, TraceEnum_ELBO, config_enumerate
label_desc: Use image node_id 2 for calling a global function with example usage: local_model(weights, locs, scale, data) and returns: pyro