code: def test_autocorrelation_vectorized():
      # make a mostly noisy x with a couple constant series
      x = torch.randn(3, 4, 5)
      x[1, 2] = 0
      x[2, 3] = 1
  
      actual = autocorrelation(x, dim=-1)
      expected = torch.tensor(
          [[autocorrelation(xij).tolist() for xij in xi] for xi in x]
      )
      assert_equal(actual, expected)
  
      assert (actual[1, 2] == 1).all()
      assert (actual[2, 3] == 1).all()
  
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 11 for calling a global function with example usage: test_autocorrelation_vectorized() without return types