code: def test_crps_empirical(num_samples, event_shape):
      truth = torch.randn(event_shape)
      pred = truth + 0.1 * torch.randn((num_samples,) + event_shape)
  
      actual = crps_empirical(pred, truth)
      assert actual.shape == truth.shape
  
      expected = (pred - truth).abs().mean(0) - 0.5 * (
          pred - pred.unsqueeze(1)
      ).abs().mean([0, 1])
      assert_close(actual, expected)
  
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 22 for calling a global function with example usage: test_crps_empirical(num_samples, event_shape) without return types