code: def test_relaxed_beta_binomial():
      total_count = torch.arange(1, 17)
      concentration1 = torch.logspace(-1, 2, 8).unsqueeze(-1)
      concentration0 = concentration1.unsqueeze(-1)
  
      d1 = beta_binomial_dist(
          concentration1, concentration0, total_count
      )
      assert isinstance(d1, dist.ExtendedBetaBinomial)
  
      with set_relaxed_distributions():
          d2 = beta_binomial_dist(
              concentration1, concentration0, total_count
          )
      assert isinstance(d2, dist.Normal)
      assert_close(d2.mean, d1.mean)
      assert_close(
          d2.variance, d1.variance.clamp(min=_RELAX_MIN_VARIANCE)
      )
  
imports: import math
  import pytest
  import torch
  from torch.distributions.transforms import SigmoidTransform
  import pyro.distributions
  from pyro.contrib.epidemiology import beta_binomial_dist, binomial_dist, infection_dist
  from pyro.contrib.epidemiology.distributions import _RELAX_MIN_VARIANCE, set_relaxed_distributions
  from tests.common import assert_close
label_desc: Use image node_id 12 for calling a global function with example usage: test_relaxed_beta_binomial() without return types