code: def model(batch, subsample, full_size):
      with ignore_jit_warnings():
          num_time_steps = len(batch)
      result = [None] * num_time_steps
      drift = pyro.sample("drift", dist.LogNormal(-1, 0.5))
      with pyro.plate("data", full_size, subsample=subsample):
          z = 0.0
          for t in range(num_time_steps):
              z = pyro.sample(
                  "state_{}".format(t), dist.Normal(z, drift)
              )
              result[t] = pyro.sample(
                  "obs_{}".format(t),
                  dist.Bernoulli(logits=z),
                  obs=batch[t],
              )
      return torch.stack(result)
  
imports: import io
  import warnings
  import pytest
  import torch
  from torch.distributions import constraints
  import pyro
  import pyro.distributions
  from pyro.contrib.easyguide import EasyGuide, easy_guide
  from pyro.infer import SVI, Trace_ELBO
  from pyro.infer.autoguide.initialization import init_to_mean, init_to_median
  from pyro.optim import Adam
  from pyro.util import ignore_jit_warnings
label_desc: Use image node_id 1 for calling a global function with example usage: model(batch, subsample, full_size) and returns: torch