code: def test_covariance_with_batching(self):
      fake_kernel = test_fixtures.TestTransitionKernel((9, 3))
      cov_reducer = covariance_reducer.CovarianceReducer(event_ndims=1)
      reducer_kernel = with_reductions.WithReductions(
          inner_kernel=fake_kernel,
          reducer=cov_reducer,
      )
      state = tf.zeros((9, 3))
      kernel_results = reducer_kernel.bootstrap_results(state)
      for _ in range(6):
          state, kernel_results = reducer_kernel.one_step(
              state, kernel_results
          )
      final_cov = cov_reducer.finalize(kernel_results.reduction_results)
      self.assertEqual(
          (9, 3), kernel_results.reduction_results.cov_state.mean.shape
      )
      self.assertEqual((9, 3, 3), final_cov.shape)
  
imports: from absl.testing import parameterized
  import numpy
  import tensorflow.compat.v2
  from tensorflow_probability.python.bijectors import exp
  from tensorflow_probability.python.distributions import mvn_diag
  from tensorflow_probability.python.distributions import mvn_tril
  from tensorflow_probability.python.experimental.mcmc import covariance_reducer
  from tensorflow_probability.python.experimental.mcmc import step
  from tensorflow_probability.python.experimental.mcmc import with_reductions
  from tensorflow_probability.python.experimental.mcmc.internal import test_fixtures
  from tensorflow_probability.python.internal import test_util
  from tensorflow_probability.python.mcmc import hmc
  from tensorflow_probability.python.mcmc import sample
  from tensorflow_probability.python.mcmc import simple_step_size_adaptation
  from tensorflow_probability.python.mcmc import transformed_kernel
label_desc: Use image node_id 2 for calling the CovarianceWithReductionsTest obj's underlying member method code with example usage: obj.test_covariance_with_batching() without return types