code: def test_batched_streaming_covariance(self):
      fake_kernel = test_fixtures.TestTransitionKernel((2, 3))
      cov_reducer = covariance_reducer.CovarianceReducer(event_ndims=1)
      reduction_rslt, last_sample, _ = sample_fold(
          num_steps=5,
          current_state=tf.convert_to_tensor(
              [[0.0, 0.0, 0.0], [0.0, 0.0, 0.0]]
          ),
          kernel=fake_kernel,
          reducer=cov_reducer,
          seed=test_util.test_seed(),
      )
      reduction_rslt = self.evaluate(reduction_rslt)
      self.assertEqual((2, 3, 3), reduction_rslt.shape)
      self.assertAllEqual(
          np.ones(reduction_rslt.shape) * 2, reduction_rslt
      )
      self.assertAllEqualNested(
          last_sample, [[5.0, 5.0, 5.0], [5.0, 5.0, 5.0]]
      )
  
imports: import collections
  import warnings
  from absl.testing import parameterized
  import numpy
  import tensorflow.compat.v2
  from tensorflow_probability.python.distributions import normal
  from tensorflow_probability.python.experimental.mcmc import covariance_reducer
  from tensorflow_probability.python.experimental.mcmc import expectations_reducer
  from tensorflow_probability.python.experimental.mcmc.internal import test_fixtures
  from tensorflow_probability.python.experimental.mcmc.sample_fold import sample_chain_with_burnin
  from tensorflow_probability.python.experimental.mcmc.sample_fold import sample_fold
  from tensorflow_probability.python.internal import samplers
  from tensorflow_probability.python.internal import tensorshape_util
  from tensorflow_probability.python.internal import test_util
  from tensorflow_probability.python.mcmc import hmc
label_desc: Use image node_id 7 for calling the SampleFoldTest obj's underlying member method code with example usage: obj.test_batched_streaming_covariance() without return types