code: def check_guide(guide):
      full_size = 50
      batch_size = 20
      num_time_steps = 8
      pyro.set_rng_seed(123456789)
      data = model(
          [None] * num_time_steps, torch.arange(full_size), full_size
      )
      assert data.shape == (num_time_steps, full_size)
  
      pyro.get_param_store().clear()
      pyro.set_rng_seed(123456789)
      svi = SVI(model, guide, Adam({"lr": 0.02}), Trace_ELBO())
      for epoch in range(2):
          beg = 0
          while beg < full_size:
              end = min(full_size, beg + batch_size)
              subsample = torch.arange(beg, end)
              batch = data[:, beg:end]
              beg = end
              svi.step(batch, subsample, full_size=full_size)
  
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 2 for calling a global function with example usage: check_guide(guide) without return types