code: def _beta_bernoulli(data):
      alpha = torch.tensor([1.1, 1.1])
      beta = torch.tensor([1.1, 1.1])
      p_latent = pyro.sample("p_latent", dist.Beta(alpha, beta))
      with pyro.plate("data", data.shape[0], dim=-2):
          pyro.sample("obs", dist.Bernoulli(p_latent), obs=data)
      return p_latent
  
imports: import io
  import logging
  import pytest
  import torch
  import pyro
  import pyro.distributions
  import pyro.poutine
  from pyro.infer import config_enumerate
  from pyro.infer.mcmc import HMC, NUTS
  from pyro.infer.mcmc.api import MCMC
  from pyro.infer.mcmc.util import TraceEinsumEvaluator, TraceTreeEvaluator, initialize_model
  from pyro.infer.reparam import LatentStableReparam
  from pyro.poutine.subsample_messenger import _Subsample
  from tests.common import assert_close, assert_equal, xfail_param
label_desc: Use image node_id 15 for calling a global function with example usage: _beta_bernoulli(data) and returns: p_latent