code: def test_score_errors_event_dim_mismatch(dist):
      for idx in dist.get_batch_data_indices():
          dist_params = dist.get_dist_params(idx)
          d = dist.pyro_dist(**dist_params)
          test_data_wrong_dims = torch.ones(d.shape() + (1,))
          if len(d.event_shape) > 0:
              if (
                  dist.get_test_distribution_name()
                  == "MultivariateNormal"
              ):
                  pytest.skip(
                      "MultivariateNormal does not do shape validation in log_prob."
                  )
              elif (
                  dist.get_test_distribution_name()
                  == "LowRankMultivariateNormal"
              ):
                  pytest.skip(
                      "LowRankMultivariateNormal does not do shape validation in log_prob."
                  )
              with pytest.raises((ValueError, RuntimeError)):
                  d.log_prob(test_data_wrong_dims)
  
imports: import numpy
  import pytest
  import torch
  import pyro
  import pyro.distributions
  from pyro.distributions import TorchDistribution
  from pyro.distributions.testing.gof import auto_goodness_of_fit
  from pyro.distributions.util import broadcast_shape
  from tests.common import TEST_FAILURE_RATE, assert_close, assert_equal, xfail_if_not_implemented
label_desc: Use image node_id 7 for calling a global function with example usage: test_score_errors_event_dim_mismatch(dist) without return types