code: def test_gelman_rubin():
      # only need to test precision for small data
      x = torch.empty(2, 10)
      x[0, :] = torch.arange(10.0)
      x[1, :] = torch.arange(10.0) + 1
  
      r_hat = gelman_rubin(x)
      assert_equal(r_hat.item(), 0.98, prec=0.01)
  
imports: import warnings
  import pytest
  import torch
  from pyro.ops.stats import _cummin, autocorrelation, autocovariance, crps_empirical, effective_sample_size, fit_generalized_pareto, gelman_rubin, hpdi, pi, quantile, resample, split_gelman_rubin, waic
  from tests.common import assert_close, assert_equal, xfail_if_not_implemented
label_desc: Use image node_id 15 for calling a global function with example usage: test_gelman_rubin() without return types