code: def test_init():
      total = 10
      counts = dist.Binomial(total, 0.3).sample()
      concentration1 = torch.tensor(0.5)
      concentration0 = torch.tensor(1.5)
  
      prior = dist.Beta(concentration1, concentration0)
      likelihood = dist.Beta(1 + counts, 1 + total - counts)
  
      def model():
          x = pyro.sample("x", prior)
          pyro.sample("counts", dist.Binomial(total, x), obs=counts)
          return x
  
      check_init_reparam(model, ConjugateReparam(likelihood))
  
imports: import pytest
  import torch
  import pyro
  import pyro.distributions
  import pyro.poutine
  from pyro.infer import Predictive, Trace_ELBO
  from pyro.infer.autoguide import AutoDiagonalNormal
  from pyro.infer.mcmc.api import MCMC
  from pyro.infer.mcmc.hmc import HMC
  from pyro.infer.reparam import ConjugateReparam, LinearHMMReparam, StableReparam
  from tests.common import assert_close
  from tests.ops.gaussian import random_mvn
  from .util import check_init_reparam
label_desc: Use image node_id 8 for calling a global function with example usage: test_init() and returns: x